unap-rafael
Andree Valle Campos
2018-01-15
1 ELISA STANDARDIZATION
1.1 Aim
- Principal:
- Implement a reproducible standardization of OD values across ELISA plates following Miura et.al., 20081.
- Secondary:
- Generate useful outputs to compare standardization quality.
1.2 To Do
- Write methods for the manuscript.
1.3 Dependencies
The required R packages for this analysis are:
##essential
library(tidyverse) # set of tidy packages (readr, tibble, dplyr, tidyr, ggplot2, ¿purr?)
library(tidyxl) # read untidy excel formats
library(readxl) # read excel files as tidy tables
library(drc) # fit dose-response models
library(mixtools) # analyze finete mixture models
##accesory
library(DiagrammeR) # create flowcharttheme_set(theme_bw())1.4 Method
- using
DiagrammeR6
#install.packages("DiagrammeR")
#library(DiagrammeR)
DiagrammeR("
graph LR
A[XLS data] -.-> |readxl+tidyxl| B{R data}
Z[CSV data] -.-> |plater| B{R data}
B --> C1[STD]
B --> E[ctr +/-]
B --> D1[UNK]
C1 -.-> |drc| C2[4pLL model]
C2 --> C3[Box-Cox]
C3 --> F{UNK Ab.units}
D1 --> D2[mean.OD]
D2 --> D3[OD %CV]
D3 --> F
F --> G1[Histogram]
F --> G2[Density]
F --> G3[QQPlot]
style Z fill:#ffffff, stroke:#000000, stroke-width:2px
style A fill:#ffffff, stroke:#000000, stroke-width:2px
style B fill:#ffffff, stroke:#000000, stroke-width:2px
style C1 fill:#ffffff, stroke:#000000, stroke-width:2px
style C2 fill:#ffffff, stroke:#000000, stroke-width:2px
style C3 fill:#ffffff, stroke:#000000, stroke-width:2px
style D1 fill:#ffffff, stroke:#000000, stroke-width:2px
style D2 fill:#ffffff, stroke:#000000, stroke-width:2px
style D3 fill:#ffffff, stroke:#000000, stroke-width:2px
style E fill:#ffffff, stroke:#000000, stroke-width:2px
style F fill:#ffffff, stroke:#000000, stroke-width:2px
style G1 fill:#ffffff, stroke:#000000, stroke-width:2px
style G2 fill:#ffffff, stroke:#000000, stroke-width:2px
style G3 fill:#ffffff, stroke:#000000, stroke-width:2px
")1.4.1 Log-Logistic (4pLL) model
The curve of the log-logistic symetric model describe the response f(x) dependent of the dose x and 04 parameters: \[ f(x)=f(x;b,c,d,e)=c+\frac{d-c}{1+\exp[b(log(x)-log(e))]}\ \] where:
cis the lower limit of the response when the dosexapproaches infinity,dis the upper limit when the dosexapproaches zero,bis the slope around the point of inflection, represented byedefined as effective dose and commmonly denoted as5:ED50,EC50orIC50for continuous responses,LD50orLC50for binomial responses, and- \(T_{50}\) for event-time responses.
1.5 Procedure
12 summary plots per ELISA Template:
- 3x3 plots of STD and UNK distribution, residual variance distribution, and model transformation.
- 1x3 plots of OD450nm, mean.OD and Ab.units distributions by Density plots.
getwd()[1] "/home/avallec/Documents/Valle_GnB/0projects/R_/elixr"1.5.1 phenotypes
#x <- tidyxl::tidy_xlsx("data-raw/raw/unap-tesis/RAFAEL-data/TEMPLATES Rafael.xlsx")$data$`TEMPLATE ELISA N°1`
x <- tidyxl::tidy_xlsx("data-raw/raw/unap-tesis/RAFAEL-data/TEMPLATES Rafael.xlsx")$data
#str(x)
y <- x[[3]] %>% #i
filter(row %in% 23:29,
col %in% 2:13) %>%
dplyr::select(address,row,col,numeric,character,local_format_id) %>%
unite(ID,c("numeric","character")) %>%
mutate(ID= stringr::str_replace(ID,"_NA|NA_", ""),
Plate= paste0("N",3)) %>% #i
dplyr::select(Plate,everything()) %>%
filter(!ID %in% c("C+","C-","NA","Blank")) #%>% group_by(ID) %>% slice(1) %>% ungroup()
y <- y[FALSE,]
#str(y)
for(i in 1:length(x)){
a <- x[[i]] %>% #i
filter(row %in% 23:29,
col %in% 2:13) %>%
dplyr::select(address,row,col,numeric,character,local_format_id) %>%
unite(ID,c("numeric","character")) %>%
mutate(ID= stringr::str_replace(ID,"_NA|NA_", ""),
Plate= paste0("N",i)) %>% #i
dplyr::select(Plate,everything()) %>%
filter(!ID %in% c("C+","C-","NA","Blank")) #%>% group_by(ID) %>% slice(1) %>% ungroup()
y <- union(y,a)
}
y <- y %>% arrange(Plate,row,col)
#y %>% dplyr::count(Plate)
#std.rawy <- y %>%
#dplyr::count(ID) %>% dplyr::arrange(desc(n))
#filter(ID==2235)
#filter(ID==1856)
#filter(ID==3942)
mutate(pheno=ifelse(local_format_id %in% c(23,17,20,18,21,19,22),"asymptomatic",
ifelse(local_format_id %in% c(68,69,70,71,72,73,24),"symptomatic",
NA_character_)
)
) #%>%
#filter(ID==2235)
#filter(pheno=="asymptomatic") #%>% dplyr::select(ID)
#mutate(pheno=ifelse(ID %in% . %>% filter(pheno=="asymptomatic") %>% dplyr::select(ID),"asym","sym"))
w <- y %>% filter(pheno=="asymptomatic") %>% dplyr::select(ID)
phe <- y %>%
mutate(pheno= ifelse(ID %in% w$ID,"asymptomatic","symptomatic")) %>% # RESPETA pheno de PLACA 1 y 2 !
mutate(igg=ifelse(local_format_id %in% c(85),"igg1",
ifelse(local_format_id %in% c(87),"igg2",
ifelse(local_format_id %in% c(88,25),"igg3",
ifelse(local_format_id %in% c(90),"igg4",
"igg")
)
)
)
) %>% #group_by(ID,igg) %>% slice(1) %>% ungroup() %>%
dplyr::select(-local_format_id#,-address,-row,-col
) %>%
arrange(Plate,row,col) %>%
mutate(Plate= as.factor(Plate)) %>%
dplyr::select(-address,-starts_with("row"),-col#,-loc
) %>%
mutate(pheno=ifelse(Plate=="N2" |
Plate=="N6" |
Plate=="N7",
"symptomatic",pheno)) # RESPETAR pheno POR PLACA (OJO: más de un pheno por paciente)
#filter(ID=="3053") # asympt in template 6 y 7
#dplyr::count(pheno,igg)
#dplyr::count(igg)
#phe# %>% dplyr::count(Plate)
#phe %>% group_by(Plate) %>% slice(1)
#phe %>% dplyr::count(Plate,pheno,igg) %>% arrange(Plate)1.5.2 data
wb_sheet <- readxl::excel_sheets("data-raw/raw/unap-tesis/RAFAEL-data/TEMPLATES Rafael.xlsx")
all <- data_frame(ID=as.character(),
OD=as.double(),
Plate=as.character(),
Type=as.character(),
Ab.unit=as.double(),
order=as.integer()
)
#str(all)
# 2 GENERATE R DATA.FRAME
for (j in 1:length(wb_sheet)) {
wb_Pf_main <- readxl::read_xlsx("data-raw/raw/unap-tesis/RAFAEL-data/TEMPLATES Rafael.xlsx",
range = "A21:M29",
sheet = j)#j
wb_Pf <- readxl::read_xlsx("data-raw/raw/unap-tesis/RAFAEL-data/TEMPLATES Rafael.xlsx",
range = "A35:M43",
sheet = j)#j
all_p <- wb_Pf_main %>%
dplyr::rename(row="X__1") %>%
gather(loc,ID,-row) %>%
mutate(loc=as.numeric(loc)) %>%
arrange(row,loc) %>%
mutate(ID= ifelse(row == "H" & loc == 9, "Blank", ID)) %>% #ANOTACION AUSCENTE EN TEMPLATES
full_join(
wb_Pf %>%
dplyr::rename(row="X__1") %>%
gather(loc,OD,-row) %>%
mutate(loc=as.numeric(loc)) %>%
arrange(row,loc)
) %>%
mutate(Plate=paste0("N",j),#j
Type=ifelse(row=="A" | ID=="Blank","std",
ifelse(ID=="C+" | ID=="C-","ctr","unk")),
Ab.unit=ifelse(row=="A",stringr::str_replace(ID,"STD 1/(.+)","\\1"),
ifelse(ID=="Blank","0",NA_character_))
) %>%
mutate(Ab.unit=as.numeric(Ab.unit)) %>%
mutate(Ab.unit=ifelse(row=="A",max(Ab.unit,na.rm=T)/Ab.unit,Ab.unit)) %>% #select(-row,-loc)
replace_na(list(ID = "na")) %>%
filter(ID!="na") %>%
mutate(order=seq(1,dim(.)[1])) %>%
dplyr::select(#-address,
-starts_with("row"),#-col,
-loc)
all <- union(all,all_p)
}
all <- all %>% arrange(Plate,order)
#all %>% dplyr::count(Type)fin <- data_frame(Plate=as.character(),
order=as.integer(),
ID=as.character(),
Type=as.character(),
Ab.unit=as.double(),
OD=as.double(),
pheno=as.character(),
igg=as.character()
)
#str(fin)
for (j in 1:length(wb_sheet)) {
fin_p <- full_join(phe %>%
filter(Plate==levels(phe$Plate)[j]) %>% #requires j
mutate(order=seq(13,12+dim(.)[1])),
all %>%
filter(Plate==levels(phe$Plate)[j]) %>% #requires j
filter(Type=="unk") %>%
dplyr::select(-Plate,-ID),
by="order") %>%
dplyr::select(Plate,order,ID,Type,Ab.unit,OD,pheno,igg)
fin <- union(fin,fin_p)
}
fin <- fin %>% arrange(Plate,order)
#fin #%>% filter(ID==3053)
#OJO!!!! MALA ANOTACIÓN
#fin %>% filter(Plate=="N2") %>% dplyr::count(pheno)
#fin %>% filter(Plate=="N2") %>% filter(pheno=="asymptomatic")
#fin %>% dplyr::count(Plate)
end <- fin %>%
union(all %>%
filter(Type!="unk") %>%
dplyr::select(Plate,ID,Type,Ab.unit,OD,order) %>%
mutate(pheno=NA_character_,
igg=NA_character_)
) %>%
arrange(Plate,order) %>%
dplyr::select(-order)
#end #%>% filter(ID==2235)1.5.3 standarization
# mod is mean od (plus sd and cv)
mod <- end %>%
filter(Type=="unk") %>%
group_by(Plate,igg,ID) %>%
summarise_at(vars(OD),c("mean","sd")) %>%
ungroup() %>%
dplyr::rename(mean.OD="mean",
sd.OD="sd") %>%
mutate(cv.OD=100*sd.OD/mean.OD) %>%
mutate(order=seq(1,dim(.)[1]))
#filter(ID=="1570")
mab <- end %>%
filter(Type=="unk") %>%
group_by(Plate,igg,ID) %>%
slice(1) %>%
ungroup() %>% #filter(ID=="1570")
mutate(order=seq(1,dim(.)[1])) %>%
full_join(mod %>% dplyr::select(order,mean.OD,sd.OD,cv.OD),
by="order") %>% #mutate(test.id= ID.x==ID.y) %>% dplyr::count(test.id)
dplyr::select(-order,-OD) %>%
mutate(ord=seq(1,dim(.)[1])) %>%
unite(code,ID,ord,sep="_",remove = F)1.5.3.1 blank issue
#mascara
blk <- end %>%
filter(ID=="Blank") %>%
group_by(Plate) %>% slice(1) %>% ungroup() %>%
dplyr::select(-OD)
#media por par de blancos por placa
std <- end %>%
filter(ID=="Blank") %>%
group_by(Plate) %>%
summarise_at(vars(OD),mean,na.rm=T) %>%
ungroup() %>%
#dplyr::rename(mean.OD="OD") %>%
full_join(blk,by="Plate") %>%
dplyr::select(Plate,ID,Type,Ab.unit,OD,everything()) %>%
union(end %>% filter(Type=="std" & ID!="Blank")) %>%
arrange(Plate,Ab.unit) %>%
mutate(Plate=as.factor(Plate))1.5.3.2 4pll per template
mab_ir <- NULL
mod_bx <- NULL
#
# new dose levels as support for the line
#mdo$Ab.units %>% summary()
new_x <- expand.grid(exp(seq(log(0.1),log(2048),length=100)))
# db to add predictions of all plates
new <- data_frame(ord=as.character(),
resp=as.double(),
p=as.double(),
pmin=as.double(),
pmax=as.double(),
Plate=as.character())
#
for (j in 1:length(levels(phe$Plate))) {
#
# 5 PARAMETER ESTIMATION 4pLL model
#
wb.m1 <- drm(OD ~ Ab.unit, Plate,
data= std %>% filter(Plate==levels(phe$Plate)[j]),#j
#data= std,
fct = LL.4(names = c("b", "c", "d", "e")))
#
wb.model <- wb.m1
# 6 BOX-COX TRANSFORMATION against RESIDUAL heterogeneity
wb.model.BX <- boxcox(wb.model,
main=expression("Optimal " ~ lambda ~ " with confidence intervals"),
plotit = FALSE)
#coefficients(wb.model.BX) %>% matrix(7,4)
mab_p <- mab %>% as.data.frame()
# 7 UNK AB.UNITS ESTIMATION by INVERSE REGRESSION
mir <- ED(wb.model.BX,
mab_p[mab_p$Plate==levels(phe$Plate)[j],"mean.OD"],#j
#wb_MEAN[1:n,5],
type = "absolute",interval = "delta",
#clevel = "Pfal",
display = FALSE)
mab_ir <- rbind(mab_ir,mir)
mod_bx <- rbind(mod_bx,coefficients(wb.model.BX))
#
# predictions and confidence intervals
pdm <- predict(wb.model.BX, newdata = new_x, interval = "confidence")
# new data with predictions
new_p <- bind_cols(new_x %>%
as.tibble() %>%
rownames_to_column(var = "ord")
, pdm %>%
as.tibble() %>%
rownames_to_column(var = "ord")
) %>%
dplyr::select(-ord1) %>%
mutate(Plate=levels(phe$Plate)[j]) %>%
dplyr::rename(resp=Var1,p=Prediction,pmin=Lower,pmax=Upper)
new <- union(new,new_p)
#
}Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
non-finite finite-difference value [2]
Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
non-finite finite-difference value [2]
Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
non-finite finite-difference value [2]
Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
non-finite finite-difference value [2]
Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
non-finite finite-difference value [2]
Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt, :
non-finite finite-difference value [2]#mab
#mab[mab$Plate==levels(phe$Plate)[1],] #%>% duplicated() %>% sum()
# 7.1 FEED UNK AB.UNITS DATA.FRAME
mdo <- mab_ir %>% as.data.frame() %>% rownames_to_column() %>%
#dplyr::rename(ord=rowname) %>%
separate(rowname,c("par","Plate","mean.OD.c"),sep = ":") %>%
rownames_to_column("ord") %>%
#mutate(ord=seq(1,dim(.)[1])) %>%
full_join(mab %>%
mutate(ord=as.character(ord)) %>%
mutate(mean.OD.c=as.character(mean.OD))
,
by = c("ord","Plate","mean.OD.c")) %>%
dplyr::select(Plate,ord,ID,code,Type,pheno,igg,mean.OD,sd.OD,cv.OD,Ab.units=Estimate,
everything(),-par,-mean.OD.c,-Ab.unit) %>%
filter(!code=="2235_137") # MANUAL FILTERING of replicate on different templates
mod_bt <- mod_bx %>% as.data.frame() %>% rownames_to_column() %>% as.tibble() %>%
dplyr::rename(Plate=rowname) %>%
mutate(Plate=stringr::str_replace(Plate,"(\\d)","N\\1"))
new <- new %>% mutate(ord=as.numeric(ord)) %>% arrange(Plate,ord)1.5.3.3 outputs
#
#fin
#end
#mod
#mab
# standard curve data
std# estimated ab unit data
mdo# estimated parameters per standard curve
mod_bt# predicted model per standard curve
new1.5.4 quality control plots
#std
ctr <- end %>% filter(Type=="ctr")
#ctr %>% filter(Plate==levels(phe$Plate)[1] & ID=="C+") %>% .$OD
#std %>% filter(Plate==levels(phe$Plate)[1] & ID=="Blank") %>% .$OD1.5.4.1 cv
mdo %>%
ggplot(aes(mean.OD,cv.OD,colour=Plate)) +
geom_point() +
coord_cartesian(ylim = c(0,100)) +
geom_hline(aes(yintercept=20),linetype="dashed",size=0.3) +
geom_vline(aes(xintercept=0.25),linetype="dashed",size=0.3) +
facet_wrap(~Plate,ncol = 4) +
labs(title="QC plot: Intra-plate coefficient of variation")
1.5.4.2 4pll
std %>%
mutate(Ab.unit=ifelse(Ab.unit==0,0.5,Ab.unit)) %>%
ggplot(aes(Ab.unit,OD)) +
geom_hline(aes(yintercept=OD,linetype=ID),data=ctr) +
geom_point(aes(colour=Plate)) +
geom_ribbon(data=new, aes(x=resp, y=p, ymin=pmin, ymax=pmax), alpha=0.2) +
geom_line(data=new, aes(x=resp, y=p, colour=Plate)) +
#coord_trans(x="log") +
scale_x_log10() +
#theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
facet_wrap(~Plate,ncol = 4) +
labs(title="QC plot: 4-parameter log-logistic model per plate")
#xlab("Ferulic acid (mM)") + ylab("Root length (cm)")std %>%
ggplot(aes(Ab.unit,OD,colour=Plate)) +
geom_point() +
facet_wrap(~Plate)std %>%
ggplot(aes(log10(Ab.unit),OD,colour=Plate)) +
geom_hline(aes(yintercept=OD,linetype=ID),ctr,size=0.3) +
geom_point() +
facet_wrap(~Plate)mdo %>%
ggplot(aes(Ab.units,mean.OD,colour=igg)) +
coord_cartesian(ylim = c(0,1)) +
geom_point() +
geom_errorbarh(aes(xmin=Lower,xmax=Upper), colour="black", size=.2) +
scale_x_log10() +
facet_wrap(~Plate+pheno,nrow = 2) +
labs(title="Estimates of antibody units (AU) per plate and phenotype")
# inverse regression method
#facet_wrap(igg~pheno,nrow = 2)1.5.5 distribution plots
1.5.5.1 linear
1.5.5.1.1 scale fix
##### ab units
a <- mdo %>%
ggplot(aes(x=Ab.units,fill=pheno)) + theme_bw() #+
#scale_x_log10()
b <- a +
geom_histogram(alpha=.5,position = "identity") + facet_grid(~igg)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/ab_hist.png")
c <- a +
geom_density(alpha=.5,position = "identity") + facet_grid(~igg)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/ab_dens.png")
#facet_wrap(transform~measure,scales = "free")
Rmisc::multiplot(b,c,cols = 1)
1.5.5.1.2 scale free
##### ab units
a <- mdo %>%
ggplot(aes(x=Ab.units,fill=pheno)) + theme_bw() #+
#scale_x_log10()
b <- a +
geom_histogram(alpha=.5,position = "identity") +
facet_wrap(~igg, scale= "free", ncol = 5)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/ab_hist.png")
c <- a +
geom_density(alpha=.5,position = "identity") +
facet_wrap(~igg, scale= "free", ncol = 5)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/ab_dens.png")
#facet_wrap(transform~measure,scales = "free")
Rmisc::multiplot(b,c,cols = 1)
##### od
a <- mdo %>%
ggplot(aes(x=mean.OD,fill=pheno)) + theme_bw() #+
#scale_x_log10()
b <- a +
geom_histogram(alpha=.5,position = "identity") + facet_grid(~igg)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/od_hist.png")
c <- a +
geom_density(alpha=.5,position = "identity") + facet_grid(~igg)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/od_dens.png")
#facet_wrap(transform~measure,scales = "free")
Rmisc::multiplot(b,c,cols = 1)1.5.5.2 log
1.5.5.2.1 scale fix
##### ab units
a <- mdo %>%
ggplot(aes(x=Ab.units,fill=pheno)) + theme_bw() +
scale_x_log10()
b <- a +
geom_histogram(alpha=.5,position = "identity") + facet_grid(~igg)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/ab_hist.png")
c <- a +
geom_density(alpha=.5,position = "identity") + facet_grid(~igg)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/ab_dens.png")
#facet_wrap(transform~measure,scales = "free")
Rmisc::multiplot(b,c,cols = 1)
1.5.5.2.2 scale free
##### ab units
a <- mdo %>%
ggplot(aes(x=Ab.units,fill=pheno)) + theme_bw() +
scale_x_log10()
b <- a +
geom_histogram(alpha=.5,position = "identity") +
facet_wrap(~igg, scale= "free", ncol = 5)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/ab_hist.png")
c <- a +
geom_density(alpha=.5,position = "identity") +
facet_wrap(~igg, scale= "free", ncol = 5)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/ab_dens.png")
#facet_wrap(transform~measure,scales = "free")
Rmisc::multiplot(b,c,cols = 1)
##### od
a <- mdo %>%
ggplot(aes(x=mean.OD,fill=pheno)) + theme_bw() +
scale_x_log10()
b <- a +
geom_histogram(alpha=.5,position = "identity") + facet_grid(~igg)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/od_hist.png")
c <- a +
geom_density(alpha=.5,position = "identity") + facet_grid(~igg)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/od_dens.png")
#facet_wrap(transform~measure,scales = "free")
Rmisc::multiplot(b,c,cols = 1)2 COVARIATES
cova <- readxl::read_xlsx("data-raw/raw/unap-tesis/RAFAEL-data/Base Rafael.xlsx",
range = "A1:P59",
sheet = 1) %>%
slice(-1) %>%
dplyr::rename(EDAD="X__1",SEXO="X__2") %>%
rename_all(funs(stringr::str_to_lower(.))) %>%
dplyr::select(codigo:gametocitos) %>%
dplyr::select(codigo,condicion="condición",edad,sexo,comunidad,fiebre)covb <- readxl::read_xlsx("data-raw/raw/unap-tesis/RAFAEL-data/DENSIDADES.xlsx",
range = "A1:E59",
sheet = 1) %>%
slice(-1) %>%
rename_all(funs(stringr::str_to_lower(.))) %>%
dplyr::select(1:4) %>%
dplyr::rename(par="parástios/ul de sangre") %>%
dplyr::select(-4) %>%
dplyr::rename(condicion="condición")2.0.0.1 merge issues
- 4 muestras con incompatibilidad de covariables en
edadysexo.- prioridad:
parasitemia - retiro de observaciones con
par=0
- prioridad:
¿variable
condición?SOLVE THIS!! AFFECTS HERE
covx <- full_join(cova, covb, by="codigo")
covx %>%
dplyr::count(codigo) %>% arrange(desc(n)) %>% filter(n!=1)#covx %>% filter(codigo=="2235" | codigo=="3053" | codigo=="9165" | codigo=="9801") %>% arrange(codigo) %>%
# dplyr::select(codigo,edad,sexo,par)
cova %>% filter(codigo=="2235" | codigo=="3053" | codigo=="9165" | codigo=="9801") %>% arrange(codigo) %>%
dplyr::select(codigo,edad,sexo)covb %>% filter(codigo=="2235" | codigo=="3053" | codigo=="9165" | codigo=="9801") %>% arrange(codigo) %>%
dplyr::select(codigo,par)# par covariates finale
covf <- covb %>% filter(!(codigo=="2235" & par==0 | codigo=="3053" & par==0 | codigo=="9165" & par==0 | codigo=="9801" & par==0)) %>%
dplyr::rename(ID=codigo)mdo %>% dplyr::count(ID)mco <- full_join(mdo,covf,by="ID") %>% #dplyr::count(pheno,condicion)
dplyr::select(-condicion) %>%
mutate(Ab.units_log=log10(Ab.units))2.0.0.2 test covariates
mcv <- covx %>%
#filter(codigo=="2235" | codigo=="3053" | codigo=="9165" | codigo=="9801") %>%
filter(!(codigo=="2235" |
codigo=="3053" |
codigo=="9165" |
codigo=="9801")) %>%
#filter(codigo=="2235" | codigo=="3053" | codigo=="9165" | codigo=="9801") %>%
arrange(codigo) %>%
dplyr::select(codigo,edad,sexo,par) %>%
dplyr::rename(ID=codigo) %>%
full_join(mdo %>%
group_by(ID) %>%
slice(1) %>%
ungroup()
,by="ID") %>%
dplyr::select(ID,edad,sexo,par,pheno) %>%
#mutate(sexo=forcats::fct_recode(sexo,
# "sex1"="1", "sex0"="0")) %>%
mutate(sexo=as.factor(sexo),
pheno=as.factor(pheno),
edad=as.numeric(edad))mcv_u <- Hmisc::upData(mcv %>% dplyr::select(-ID),
labels = c(edad="Age",
sexo="Sex",
par="Parasite density"
),
units = c(edad="(years)",
par="(par/uL)"
),
levels = list(pheno=list("Asymptomatic"="asymptomatic",
"Symptomatic"="symptomatic"),
sexo=list("Female"="0",
"Male"="1") #???
)
)Input object size: 3344 bytes; 4 variables 53 observations
New object size: 4608 bytes; 4 variables 53 observationsHmisc::html(Hmisc::contents(mcv_u), maxlevels=10, levelType='table')Data frame:mcv_u
53 observations and 4 variables, maximum # NAs:4| Name | Labels | Units | Levels | Class | Storage | NAs |
|---|---|---|---|---|---|---|
| edad | Age | (years) | integer | integer | 4 | |
| sexo | Sex | 2 | integer | 4 | ||
| par | Parasite density | (par/uL) | integer | integer | 4 | |
| pheno | 2 | integer | 0 |
| Variable | Levels |
|---|---|
| sexo | Female |
| Male | |
| pheno | Asymptomatic |
| Symptomatic |
s1 <- Hmisc::summaryM(sexo + edad + par ~ pheno,
data=mcv_u,
overall=FALSE, test=TRUE)Error: `x` must be a numeric or a character vectorHmisc::latex(s1, caption='Sample covariates',
exclude1=TRUE, #npct='both',
test=TRUE , prtest="P",file="",
digits=3, prn=FALSE,
#prmsd=TRUE, brmsd=TRUE, #msdsize=mu$smaller2, #NOT-EVALUATE if PDF
middle.bold=TRUE, long = FALSE,
#legend.bottom = TRUE, #insert.bottom = TRUE,
what="%", html = TRUE,
width="100%"
) #change here for LaTeX PDFError in Hmisc::latex(s1, caption = "Sample covariates", exclude1 = TRUE, :
object 's1' not foundreadr::write_rds(mcv_u,"data/unap-rafa-covar.rds")3 SEROLOGICAL CLASSIFICATION
3.1 To Do
- Implement the mean / ROC / mixture models method
3.2 mixtools
3.2.1 check distributions
a <- mco %>% #filter(igg=="igg3") %>%
ggplot(aes(x=Ab.units
#,fill=pheno
#,fill=igg
)) + theme_bw() +
#scale_x_log10() +
geom_density(alpha=.5,position = "identity") +
geom_histogram(aes(x=Ab.units,..density..),
alpha=.5,position = "identity") +
#theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title="AU linear distribution")
b <- mco %>% #filter(igg=="igg3") %>%
ggplot(aes(sample=Ab.units
#,fill=pheno
#,fill=igg
)) +
geom_qq(alpha=.2) +
geom_qq_line(line.p = c(0.25, 0.75)) +
labs(title="Gaussian quantile-quantile plot") +
coord_cartesian(ylim = c(0,2000))
Rmisc::multiplot(a,b,cols = 2)
mco %>%
ggplot(aes(x=Ab.units
#,fill=pheno
)) + theme_bw() +
#scale_x_log10() +
geom_density(alpha=.5,position = "identity",adjust= 1/2
) +
geom_histogram(aes(x=Ab.units,..density..),
alpha=.5,position = "identity") +
facet_wrap(~igg, scales = "free",ncol = 5) +
labs(title="AU linear distribution per IgG subtype")
3.2.2 apply mixtools
# source: http://tinyheero.github.io/2015/10/13/mixture-model.html
f <- mco %>% mutate(igg=as.factor(igg)) %>%
mutate(igg=forcats::fct_relevel(igg,"igg","igg1","igg2","igg3")) %>% .$igg
#library("mixtools")
#' Plot a Mixture Component
#'
#' @param x Input data
#' @param mu Mean of component
#' @param sigma Standard deviation of component
#' @param lam Mixture weight of component
plot_mix_comps <- function(x, mu, sigma, lam) {
lam * dnorm(x, mu, sigma)
}
set.seed(1)
#length(levels(f))
#wait <- mco %>% filter(igg=="igg2") %>%
# .$Ab.units %>% log()
llmix <- data_frame(igg=as.character(),
kpr=as.numeric(),
lik=as.numeric())
for (j in 2:3) {
mixmdl_p <- normalmixEM(mco %>%
#filter(igg==levels(f)[i]) %>%
.$Ab.units #%>% log10()
,
k = j)
llmix <- llmix %>%
union(data_frame(igg="all",#levels(f)[i],
kpr=j,
lik=mixmdl_p$loglik))
}number of iterations= 37
number of iterations= 60 llmix %>% arrange(igg,desc(lik)) %>% mutate(aic=2*kpr-2*lik)#
llmix <- data_frame(igg=as.character(),
kpr=as.numeric(),
lik=as.numeric())
for (i in 1:length(levels(f))) {
for (j in 2:3) {
mixmdl_p <- normalmixEM(mco %>%
filter(igg==levels(f)[i]) %>%
.$Ab.units #%>% log10()
,
k = j)
llmix <- llmix %>%
union(data_frame(igg=levels(f)[i],
kpr=j,
lik=mixmdl_p$loglik))
}
}number of iterations= 23
One of the variances is going to zero; trying new starting values.
One of the variances is going to zero; trying new starting values.
One of the variances is going to zero; trying new starting values.
number of iterations= 81
number of iterations= 60
number of iterations= 68
number of iterations= 36
number of iterations= 79
number of iterations= 20
number of iterations= 527
number of iterations= 15
number of iterations= 40 llmix %>% arrange(igg,desc(lik)) %>% mutate(aic=2*kpr-2*lik) #%>% #group_by(igg) %>% filter(lik==max(lik))
#mixmdl <- normalmixEM(wait, k = 3)
#mixmdl$loglik### FOR ALL AB.UNITS (NO IGG SUBTYPES)
set.seed(1)
#for (i in 1:length(levels(f))) {
wait <- mco %>% #filter(igg==levels(f)[i]) %>%
.$Ab.units #%>% log10()
mixmdl <- normalmixEM(wait, k = 3)number of iterations= 41 ###
r <- data.frame(x = mixmdl$x) %>%
ggplot() +
geom_histogram(aes(x, ..density..),
#binwidth = 1,
#colour = "black",
#fill = "gray",
alpha=.5,position = "identity"
) +
stat_function(geom = "line",
fun = plot_mix_comps,
args = list(mixmdl$mu[1],
mixmdl$sigma[1],
lam = mixmdl$lambda[1]),
colour = "green", lwd = 1.5) +
stat_function(geom = "line",
fun = plot_mix_comps,
args = list(mixmdl$mu[2],
mixmdl$sigma[2],
lam = mixmdl$lambda[2]),
colour = "blue", lwd = 1.5) +
stat_function(geom = "line",
fun = plot_mix_comps,
args = list(mixmdl$mu[3],
mixmdl$sigma[3],
lam = mixmdl$lambda[3]),
colour = "red", lwd = 1.5) +
ylab("Density") +
xlab("Ab.units") +
labs(title= paste0("Ab.units: ",
#ifelse(levels(f)[i]=="igg","IgG ",
# ifelse(levels(f)[i]=="igg1","IgG1 ",
# ifelse(levels(f)[i]=="igg2","IgG2 ",
# ifelse(levels(f)[i]=="igg3","IgG3 ","IgG4 ")
# )
# )
# ),
"3-component distribution"
#,": LogLik=",
#mixmdl$loglik %>% format(digits=3)
))
#+ scale_x_log10()
####
u <- 0.90 # 90% classification probability
post.df <- as.data.frame(cbind(x = mixmdl$x, mixmdl$posterior)) %>%
mutate(comp.12=comp.1+comp.2) %>% # sum probabilities of s+ and s++
mutate(label = ifelse(comp.3 > u, "s-",
ifelse(comp.12 > u, "s+", "s0"
#ifelse(comp.1 > u,"s++","s0")
))) %>%
mutate(label=forcats::fct_relevel(label,"s-","s0","s+"#,"s++"
))
s <- post.df %>%
ggplot(aes(x = factor(label))) +
geom_bar() +
xlab("Component") +
ylab("Number of Data Points") +
labs(title="Classification")
###
t <- post.df %>%
ggplot() +
#geom_line(aes(x,comp.1), colour="green", lwd = 1.5) +
#geom_line(aes(x,comp.2), colour="blue", lwd = 1.5) +
geom_line(aes(x,comp.12), colour="blue", lwd = 1.5) +
geom_line(aes(x,comp.3), colour="red", lwd = 1.5) +
geom_hline(yintercept = u, col = "black") +
#geom_vline(xintercept = cutoffs[2], col = "black", lty=3) +
#geom_vline(xintercept = cutoffs[1], col = "black", lty=3) +
xlab("Ab.units") +
ylab("classification probability") +
labs(title="Cutoff")
Rmisc::multiplot(r,t,s,cols = 3)
#}
#sum(mco$Ab.units_log == mixmdl$x)
#length(mixmdl$x)
#sum(!post.df$x==mco$Ab.units_log)
msr <- inner_join(mco %>% rownames_to_column(),
post.df %>%
#dplyr::rename(Ab.units_log=x) %>%
dplyr::select(#Ab.units_log,
label) %>%
rownames_to_column(),
by="rowname") #%>%
#mutate(test= Ab.units_log.x==Ab.units_log.y) %>% dplyr::count(test)
msr <- msr[FALSE,] #%>% glimpse()3.2.3 2-component
set.seed(1)
for (i in 1:length(levels(f))) {
wait <- mco %>% filter(igg==levels(f)[i]) %>%
.$Ab.units #%>% log10()
mixmdl <- normalmixEM(wait, k = 3)
###
r <- data.frame(x = mixmdl$x) %>%
ggplot() +
geom_histogram(aes(x, ..density..),
#binwidth = 1,
#colour = "black",
#fill = "gray",
alpha=.5,position = "identity"
) +
stat_function(geom = "line",
fun = plot_mix_comps,
args = list(mixmdl$mu[1],
mixmdl$sigma[1],
lam = mixmdl$lambda[1]),
colour = "red", lwd = 1.5) +
stat_function(geom = "line",
fun = plot_mix_comps,
args = list(mixmdl$mu[2],
mixmdl$sigma[2],
lam = mixmdl$lambda[2]),
colour = "blue", lwd = 1.5) +
#stat_function(geom = "line",
# fun = plot_mix_comps,
# args = list(mixmdl$mu[3],
# mixmdl$sigma[3],
# lam = mixmdl$lambda[3]),
# colour = "green", lwd = 1.5) +
ylab("Density") +
xlab("Ab.units") +
labs(title= paste0(ifelse(levels(f)[i]=="igg","IgG: ",
ifelse(levels(f)[i]=="igg1","IgG1: ",
ifelse(levels(f)[i]=="igg2","IgG2: ",
ifelse(levels(f)[i]=="igg3","IgG3 ","IgG4: ")
)
)
),
"3-component distribution"
#,": LogLik=",
#mixmdl$loglik %>% format(digits=3)
))
#+ scale_x_log10()
####
u <- 0.90 # 90% classification probability
post.df <- as.data.frame(cbind(x = mixmdl$x, mixmdl$posterior)) %>%
mutate(comp.sp=ifelse(mean(comp.2)>100,
comp.2+comp.3,
comp.1+comp.2)) %>% # sum probabilities of s+ and s++
mutate(label = ifelse(comp.1 > u, "s-",
#ifelse(comp.sp > u, "s+", "s0"
ifelse(comp.2 > u, "s+", "s0"
#ifelse(comp.1 > u,"s++","s0")
))) %>%
mutate(label=forcats::fct_relevel(label,"s-","s0","s+"#,"s++"
))
s <- post.df %>%
ggplot(aes(x = factor(label))) +
geom_bar() +
xlab("Component") +
ylab("Number of Data Points") +
labs(title="Classification")
###
t <- post.df %>%
ggplot() +
#geom_line(aes(x,comp.1), colour="green", lwd = 1.5) +
geom_line(aes(x,comp.2), colour="blue", lwd = 1.5) +
#geom_line(aes(x,comp.sp), colour="blue", lwd = 1.5) +
geom_line(aes(x,comp.1), colour="red", lwd = 1.5) +
geom_hline(yintercept = u, col = "black") +
#geom_vline(xintercept = 63.6, col = "black", lty=3) +
#geom_vline(xintercept = 69.7, col = "black", lty=3) +
xlab("Ab.units") +
ylab("classification probability") +
labs(title="Cutoff")
###
msr_p <- inner_join(mco %>%
filter(igg==levels(f)[i]) %>%
rownames_to_column(),
post.df %>%
#dplyr::rename(Ab.units_log=x) %>%
dplyr::select(#Ab.units_log,
label) %>%
rownames_to_column(),
by="rowname") #%>%
#mutate(test= Ab.units_log.x==Ab.units_log.y) %>% dplyr::count(test)
msr <- union(msr, msr_p)
Rmisc::multiplot(r,t,s,cols = 3)
}3.2.4 three component distribution
- Possible mistake for IgG2 and IgG4:
- Definition of S- using only the 1st component and S+ using the 2nd and 3rd one, as was standardized for all the
igg subtypes. - CORRECTION: If 2nd component have a mean AU lower than an ARBITRARY THRESHOLD, define S+ as the sum of only the 3rd component probabilities.
- RESULT: a threshold of 100 give results as expected for
igg2andigg4seropositivity.- IN REFERENCE: Rouhani 2015 (figure 2).
- ACHIEVED CRITERIA: lower proportion of indetermined serology
s0
- ALTERNATIVE: a threshold of 40 may be convinient for
igg4in order to explain its:- significantly higher mean AU in symptomatics, and
- strong association to symptomatic susceptibility.
- According to the component CLASSIFICATION CRITERIA based on the proportion of
s0, which is higher than the previous threshold, this is not the best classification. - However, if a boost or other phenomena (including a different genotype within the study population) that increase the proportion of IgG4 during a symptomatic episode is assumed, this may be the right one.
- Interestingly, this 2nd component is full of symptomatic samples.
- RESULT: a threshold of 100 give results as expected for
- NOTE: Even though the mean of the 2nd component is higher than 100, for
iggthe threshold of seropositivity is lower than 40 AU, under the right criteria.
- Definition of S- using only the 1st component and S+ using the 2nd and 3rd one, as was standardized for all the
set.seed(1)
# i= 3
for (i in 1:length(levels(f))) {
wait <- mco %>% filter(igg==levels(f)[i]) %>%
.$Ab.units #%>% log10()
mixmdl <- normalmixEM(wait, k = 3)
###
r <- data.frame(x = mixmdl$x) %>%
ggplot() +
geom_histogram(aes(x, ..density..),
#binwidth = 1,
#colour = "black",
#fill = "gray",
alpha=.5,position = "identity"
) +
stat_function(geom = "line",
fun = plot_mix_comps,
args = list(mixmdl$mu[1],
mixmdl$sigma[1],
lam = mixmdl$lambda[1]),
colour = "red", lwd = 1.5) +
stat_function(geom = "line",
fun = plot_mix_comps,
args = list(mixmdl$mu[2],
mixmdl$sigma[2],
lam = mixmdl$lambda[2]),
colour = "blue", lwd = 1.5) +
stat_function(geom = "line",
fun = plot_mix_comps,
args = list(mixmdl$mu[3],
mixmdl$sigma[3],
lam = mixmdl$lambda[3]),
colour = "green", lwd = 1.5) +
ylab("Density") +
xlab("Ab.units") +
labs(title= paste0(ifelse(levels(f)[i]=="igg","IgG: ",
ifelse(levels(f)[i]=="igg1","IgG1: ",
ifelse(levels(f)[i]=="igg2","IgG2: ",
ifelse(levels(f)[i]=="igg3","IgG3 ","IgG4: ")
)
)
),
"3-component distribution"
#,": LogLik=",
#mixmdl$loglik %>% format(digits=3)
))
#+ scale_x_log10()
####
u <- 0.90 # 90% classification probability
post.df <- as.data.frame(cbind(x = mixmdl$x, mixmdl$posterior)) %>%
#mutate(comp.12=comp.1+comp.2,
# comp.23=comp.2+comp.3) %>%
mutate(comp.sp=if_else(rep(mixmdl$mu[2]>40,length(mixmdl$x)), # UMBRAL ARBITRARIO!!
comp.2+comp.3, # sero+ equals to the sum of comp 2+3
comp.3)) %>% # sero+ equals to the sum of comp 3
mutate(comp.sn=if_else(rep(mixmdl$mu[2]>40,length(mixmdl$x)),
comp.1, # sero- equals to the sum of comp 1
comp.1+comp.2)) %>% # sero+ equals to the sum of comp 1+2
mutate(label = if_else(comp.sn > u, "s-",
ifelse(comp.sp > u, "s+", "s0"
#ifelse(comp.2 > u, "s+", #"s0"
#ifelse(comp.1 > u,"s++","s0")
))) %>%
mutate(label=forcats::fct_relevel(label,"s-","s0","s+"#,"s++"
))
s <- post.df %>%
ggplot(aes(x = factor(label))) +
geom_bar() +
xlab("Component") +
ylab("Number of Data Points") +
labs(title="Classification")
###
t <- post.df %>%
ggplot() +
#geom_line(aes(x,comp.1), colour="green", lwd = 1.5) +
#geom_line(aes(x,comp.2), colour="blue", lwd = 1.5) +
#geom_point(aes(x,comp.sp), colour="blue", lwd = 1.5) +
geom_line(aes(x,comp.sp), colour="blue", lwd = 1.5) +
#geom_point(aes(x,comp.sn), colour="red", lwd = 1.5) +
geom_line(aes(x,comp.sn), colour="red", lwd = 1.5) +
geom_hline(yintercept = u, col = "black") +
#geom_vline(xintercept = 63.6, col = "black", lty=3) +
#geom_vline(xintercept = 69.7, col = "black", lty=3) +
xlab("Ab.units") +
ylab("classification probability") +
labs(title="Cutoff")
###
msr_p <- inner_join(mco %>%
filter(igg==levels(f)[i]) %>%
rownames_to_column(),
post.df %>%
#dplyr::rename(Ab.units_log=x) %>%
dplyr::select(#Ab.units_log,
label) %>%
rownames_to_column(),
by="rowname") #%>%
#mutate(test= Ab.units_log.x==Ab.units_log.y) %>% dplyr::count(test)
msr <- union(msr, msr_p)
Rmisc::multiplot(r,t,s,cols = 3)
}One of the variances is going to zero; trying new starting values.
One of the variances is going to zero; trying new starting values.
number of iterations= 71
number of iterations= 73
number of iterations= 22
number of iterations= 475
number of iterations= 34 
4 DATA FRAME
#mco
#msrglimpse(
msr %>%
mutate(ord=as.numeric(ord)) %>% arrange(ord) %>%
dplyr::select(-Ab.units_log, -rowname)
)Observations: 232
Variables: 16
$ Plate <chr> "N1", "N1", "N1", "N1", "N1", "N1", "N1", "N1", "N1", "N1", "N1"...
$ ord <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 1...
$ ID <chr> "1570", "1843", "1856", "1910", "1969", "1977", "2131", "2134", ...
$ code <chr> "1570_1", "1843_2", "1856_3", "1910_4", "1969_5", "1977_6", "213...
$ Type <chr> "unk", "unk", "unk", "unk", "unk", "unk", "unk", "unk", "unk", "...
$ pheno <chr> "asymptomatic", "symptomatic", "asymptomatic", "asymptomatic", "...
$ igg <chr> "igg", "igg", "igg", "igg", "igg", "igg", "igg", "igg", "igg", "...
$ mean.OD <dbl> 0.5485, 0.1825, 0.6260, 0.3070, 0.6220, 0.5310, 0.6615, 0.5795, ...
$ sd.OD <dbl> 0.0205060967, 0.0007071068, 0.0141421356, 0.0098994949, 0.000000...
$ cv.OD <dbl> 3.7385773, 0.3874558, 2.2591271, 3.2245912, 0.0000000, 7.1909164...
$ Ab.units <dbl> 94.496989, 9.591621, 182.796839, 23.028486, 175.224706, 83.84476...
$ `Std. Error` <dbl> 27.4859252, 1.2766621, 63.3962207, 4.1773628, 60.1332973, 23.554...
$ Lower <dbl> 32.319506, 6.703611, 39.384624, 13.578635, 39.193737, 30.560885,...
$ Upper <dbl> 156.674471, 12.479631, 326.209054, 32.478337, 311.255675, 137.12...
$ par <dbl> 4020, 1960, 1078, 406, 0, 2716, 498, 4866, 1094, 2010, 621, 824,...
$ label <fctr> s+, s0, s+, s+, s+, s+, s+, s+, s-, s+, s+, s+, s-, s+, s+, s+,...#summary(mdo)
readr::write_csv(
msr %>%
mutate(ord=as.numeric(ord)) %>% arrange(ord) %>%
dplyr::select(-Ab.units_log, -rowname)
,"data/unap-rafael.csv")
readr::write_rds(
msr %>%
mutate(ord=as.numeric(ord)) %>% arrange(ord) %>%
dplyr::select(-Ab.units_log, -rowname)
, "data/unap-rafael.rds")5 STATISTICAL ANALYSIS PLAN
5.1 To Do
- Do it
5.2 Sample size
mco %>%
group_by(igg) %>% dplyr::count(pheno)5.3 Test Hypothesis
5.3.1 Mean AU
f <- mco %>% mutate(igg=as.factor(igg)) %>% .$igg
#mco <- mco %>%
# #dplyr::select(Ab.units,par) %>%
# mutate(Ab.units=log10(Ab.units),par=log10(par)) %>%
# filter(par!=-Inf) #%>%
stt <- data_frame(##estimate=as.double(),
##estimate1=as.double(),
##estimate2=as.double(),
statistic=as.double(),
p.value=as.double(),
#parameter=as.double(),#
#conf.low=as.double(),#
#conf.high=as.double(),#
method=as.factor(NULL),
alternative=as.factor(NULL),
igg=as.character(),
lab=as.character())
for (l in 1:length(levels(f))) {
## Primero, Prueba de Hipótesis para comparar varianzas:
i <- mco %>% filter(igg==levels(f)[l]) %>%
var.test(Ab.units_log ~ pheno, data = .) %>% broom::tidy()
## Rpta: Bajo n.s. 0.05, F cae en Región de no-Rechazo de Hipótesis Nula (RnoRHo)
## Conclusión: Supuesto de igualdad de varianzas poblacionales SÍ es válido
## Segundo, Prueba de Hipótesis para comparar medias:
j <- mco %>% filter(igg==levels(f)[l]) %>%
#t.test(Ab.units_log ~ pheno, data = ., var.equal= i$p.value>0.05) %>%
#wilcox.test(Ab.units_log ~ pheno, data = .) %>%#,
wilcox.test(Ab.units ~ pheno, data = .) %>%#,
#conf.int=TRUE) %>%
broom::tidy() %>% #%>% format(digits=2)
dplyr::select(-starts_with("estimate")) %>%
mutate(igg=levels(f)[l],
lab= paste0(#"t=",#should be done -> mix variance equality test
#"W=",#non-parametric used in literature
#statistic %>% format(digits=2),
#", df=",parameter %>% format(digits=2),
#", P",ifelse(p.value<0.001,"<0.001",
"italic('P')",ifelse(p.value<0.001,"<0.001",
paste0("==",p.value %>% format(digits=2))
)
)
)
stt <- stt %>% union(j)
}
stt <- stt %>% arrange(igg) %>% dplyr::select(igg,everything()) %>%
mutate(x=.5,
y=.15) %>%
mutate(igg=forcats::fct_recode(igg,
"IgG"="igg",
"IgG1"="igg1",
"IgG2"="igg2",
"IgG3"="igg3",
"IgG4"="igg4"
))
# plot
mco %>% #filter(igg==levels(f)[1]) %>%
mutate(igg=forcats::fct_recode(igg,
"IgG"="igg",
"IgG1"="igg1",
"IgG2"="igg2",
"IgG3"="igg3",
"IgG4"="igg4"
),
pheno=forcats::fct_recode(pheno,
"Asymptomatic"="asymptomatic",
"Symptomatic"="symptomatic")) %>%
ggplot(aes(pheno,Ab.units)) +
geom_boxplot(#position=position_dodge(0.8)
) +
geom_dotplot(#aes(fill=sev_WHO),
binaxis='y', stackdir='center', alpha=.3,
dotsize=1#, position=position_dodge(0.8)
) +
facet_wrap(~igg,ncol = 5) +
geom_text(aes(x,y,label=lab),data = stt,parse = T,
#vjust=.5,hjust=0.05,size=3.5) +
vjust=.5,hjust=0.05,size=3.5) + # if log10, then change it.
#coord_cartesian(ylim = c(-300,2000)) + # if log10, then change it.
scale_y_log10() +
#labs(title="Test AU mean equality among phenotypes per IgG subtypes") +
xlab("Carrier") + ylab("Antibody units (log scale)")
- nota:
- prueba de hipotesis no-paramétrica
- ploteo en escala logística
msr %>% group_by(igg,pheno) %>% dplyr::summarise(min=min(Ab.units),
q25=quantile(Ab.units) %>% .[2],
mean=mean(Ab.units),
q50=quantile(Ab.units) %>% .[3],
q75=quantile(Ab.units) %>% .[4],
max=max(Ab.units))5.3.2 Proportion sero+
msr %>% dplyr::count(igg,label) %>%
group_by(igg) %>% mutate(tot=sum(n),prop= (n*100)/(sum(n))) %>%
filter(label=="s+")msr %>%
mutate(label=forcats::fct_relevel(label,"s-","s+","s0"#,"s++"
)) %>%
ggplot(aes(x=igg,fill=label)) +
geom_bar(position = "fill") +
#facet_grid(~igg) +
labs(title="Proportion of seropositives per IgG subtype")
msr %>% dplyr::count(igg,pheno,label) %>%
group_by(igg,pheno) %>% mutate(tot=sum(n),prop= (n*100)/(sum(n))) %>%
filter(label=="s+") #%>% #ungroup() %>%
#dplyr::select(-n,-label, -tot) %>%
#spread(pheno,prop)msr %>%
dplyr::select(pheno,igg,label) %>%
#filter(igg==levels(f)[i]) %>%
#dplyr::select(-igg) %>%
group_by(igg,pheno,label) %>% dplyr::summarise(n=n()) %>% ungroup() %>%
spread(label,n)ftt <- data_frame(igg=as.character(),
p.value=as.double(),
#estimate=as.double(),
method=as.character()#,
#alternative=as.factor()
)
for (i in 1:length(levels(f))) {
fst <- msr %>%
filter(!label=="s0") %>%
dplyr::select(pheno,igg,label) %>%
filter(igg==levels(f)[i]) %>%
dplyr::select(-igg) %>%
group_by(pheno,label) %>% dplyr::summarise(n=n()) %>% ungroup() %>%
#spread(label,n) %>% as.matrix() %>%
reshape2::acast(pheno ~ label,
value.var = "n") #%>% #class()
fst[is.na(fst)] <- 0
ftt <- ftt %>%
union(
fisher.test(fst) %>% broom::tidy() %>%
mutate(igg=levels(f)[i]) %>%
dplyr::select(igg,p.value,
#estimate,
method#,alternative
)
)
}
ftt %>% arrange(igg) %>%
mutate(sig=ifelse(p.value<0.05,"signif","not-signif"))fttt <- ftt %>% arrange(igg) %>% dplyr::select(igg,everything()) %>%
mutate(x=.5,
y=-.05) %>%
mutate(lab= paste0("italic('P')",ifelse(p.value<0.001,"<0.001",
paste0("==",p.value %>% format(digits=2))
)
)
) %>%
mutate(lab=stringr::str_replace(lab,"(P = 0\\...)(.+)","\\1")) %>%
mutate(igg=forcats::fct_recode(igg,
"IgG"="igg",
"IgG1"="igg1",
"IgG2"="igg2",
"IgG3"="igg3",
"IgG4"="igg4"
))msr %>%
mutate(igg=forcats::fct_recode(igg,
"IgG"="igg",
"IgG1"="igg1",
"IgG2"="igg2",
"IgG3"="igg3",
"IgG4"="igg4"
),
pheno=forcats::fct_recode(pheno,
"Asymptomatic"="asymptomatic",
"Symptomatic"="symptomatic")) %>%
mutate(label=forcats::fct_relevel(label,"s-","s+","s0"#,"s++"
)) %>%
full_join(fttt %>% dplyr::select(igg,lab,y,x),by = "igg") %>%
mutate(lab=if_else(label=="s+",lab,NA_character_)) %>%
arrange(lab) %>%
mutate(lab=if_else(igg==lead(igg),NA_character_,lab)) %>%
filter(!label=="s0") %>%
ggplot(aes(x=pheno,fill=label)) +
geom_bar(position = "fill") +
facet_grid(~igg) +
coord_cartesian(ylim = c(-.12,1)) +
geom_text(aes(x=x,
y=y,label=lab),parse = T,#position = "fill",#data = fttt,,
##vjust=.5,hjust=0.05,size=3.5) +
vjust=1,hjust=0.0005,size=3.5
) + # if log10, then change it.
#labs(title="Proportion of seropositives among phenotypes per IgG subtype") +
xlab("Carrier") + ylab("Proportion") +
scale_fill_grey(#start = 0, end = .9,
name="Serology",labels=c("Negative","Positive"#,"Indeterminate"
))
##aqui
s1 <- Hmisc::summaryM(label
~ pheno,
data=msr %>% filter(igg=="igg"),
overall=FALSE, test=TRUE)
Hmisc::latex(s1, caption='Sample covariates',
exclude1=TRUE, npct='both',
digits=3,
#prmsd=TRUE, brmsd=TRUE, #msdsize=mu$smaller2, #NOT-EVALUATE if PDF
middle.bold=TRUE, long = TRUE,
#legend.bottom = TRUE, #insert.bottom = TRUE,
what="%", html = TRUE, width="100%"
) #change here for LaTeX PDF| Sample covariates. | |||
| asymptomatic N=27 |
symptomatic N=29 |
Test Statistic |
|
|---|---|---|---|
| label | χ22=3.41, P=0.182 | ||
| s- | 7% 2⁄27 | 24% 7⁄29 | |
| s0 | 4% 1⁄27 | 7% 2⁄29 | |
| s+ | 89% 24⁄27 | 69% 20⁄29 | |
|
Test used: Pearson test . | |||
##aqui
s1 <- Hmisc::summaryM(label
~ pheno,
data=msr %>% filter(igg=="igg1"),
overall=FALSE, test=TRUE)
Hmisc::latex(s1, caption='Sample covariates',
exclude1=TRUE, npct='both',
digits=3,
#prmsd=TRUE, brmsd=TRUE, #msdsize=mu$smaller2, #NOT-EVALUATE if PDF
middle.bold=TRUE, long = TRUE,
#legend.bottom = TRUE, #insert.bottom = TRUE,
what="%", html = TRUE, width="100%"
) #change here for LaTeX PDF| Sample covariates. | |||
| asymptomatic N=24 |
symptomatic N=20 |
Test Statistic |
|
|---|---|---|---|
| label | χ22=2.18, P=0.336 | ||
| s- | 12% 3⁄24 | 30% 6⁄20 | |
| s0 | 17% 4⁄24 | 10% 2⁄20 | |
| s+ | 71% 17⁄24 | 60% 12⁄20 | |
|
Test used: Pearson test . | |||
##aqui
s1 <- Hmisc::summaryM(label
~ pheno,
data=msr %>% filter(igg=="igg2"),
overall=FALSE, test=TRUE)
Hmisc::latex(s1, caption='Sample covariates',
exclude1=TRUE, npct='both',
digits=3,
#prmsd=TRUE, brmsd=TRUE, #msdsize=mu$smaller2, #NOT-EVALUATE if PDF
middle.bold=TRUE, long = TRUE,
#legend.bottom = TRUE, #insert.bottom = TRUE,
what="%", html = TRUE, width="100%"
) #change here for LaTeX PDF| Sample covariates. | |||
| asymptomatic N=24 |
symptomatic N=20 |
Test Statistic |
|
|---|---|---|---|
| label | χ22=4.73, P=0.094 | ||
| s- | 83% 20⁄24 | 70% 14⁄20 | |
| s0 | 8% 2⁄24 | 0% 0⁄20 | |
| s+ | 8% 2⁄24 | 30% 6⁄20 | |
|
Test used: Pearson test . | |||
##aqui
s1 <- Hmisc::summaryM(label
~ pheno,
data=msr %>% filter(igg=="igg3"),
overall=FALSE, test=TRUE)
Hmisc::latex(s1, caption='Sample covariates',
exclude1=TRUE, npct='both',
digits=3,
#prmsd=TRUE, brmsd=TRUE, #msdsize=mu$smaller2, #NOT-EVALUATE if PDF
middle.bold=TRUE, long = TRUE,
#legend.bottom = TRUE, #insert.bottom = TRUE,
what="%", html = TRUE, width="100%"
) #change here for LaTeX PDF| Sample covariates. | |||
| asymptomatic N=24 |
symptomatic N=20 |
Test Statistic |
|
|---|---|---|---|
| label | χ22=1.59, P=0.451 | ||
| s- | 29% 7⁄24 | 20% 4⁄20 | |
| s0 | 0% 0⁄24 | 5% 1⁄20 | |
| s+ | 71% 17⁄24 | 75% 15⁄20 | |
|
Test used: Pearson test . | |||
##aqui
s1 <- Hmisc::summaryM(label
~ pheno,
data=msr %>% filter(igg=="igg4"),
overall=FALSE, test=TRUE)
Hmisc::latex(s1, caption='Sample covariates',
exclude1=TRUE, npct='both',
digits=3,
#prmsd=TRUE, brmsd=TRUE, #msdsize=mu$smaller2, #NOT-EVALUATE if PDF
middle.bold=TRUE, long = TRUE,
#legend.bottom = TRUE, #insert.bottom = TRUE,
what="%", html = TRUE, width="100%"
) #change here for LaTeX PDF| Sample covariates. | |||
| asymptomatic N=24 |
symptomatic N=20 |
Test Statistic |
|
|---|---|---|---|
| label | χ22=25.4, P<0.001 | ||
| s- | 54% 13⁄24 | 0% 0⁄20 | |
| s0 | 21% 5⁄24 | 0% 0⁄20 | |
| s+ | 25% 6⁄24 | 100% 20⁄20 | |
|
Test used: Pearson test . | |||
5.3.3 Distribution sero+
- the minimum value of the sero+ distribution is close to the cut-off and could be assumed as its value, but is not exactly that value.
msr %>%
mutate(label=forcats::fct_relevel(label,"s-","s+","s0"#,"s++"
)) %>%
ggplot(aes(x=Ab.units,fill=label)) +
#geom_bar(position = "fill") +
geom_histogram(position = "stack") +
facet_grid(~igg,scales = "free") +
labs(title="Proportion of seropositives")
msr %>%
filter(label=="s+") %>%
group_by(igg) %>% dplyr::summarise(min=min(Ab.units),
q25=quantile(Ab.units) %>% .[2],
mean=mean(Ab.units),
q50=quantile(Ab.units) %>% .[3],
q75=quantile(Ab.units) %>% .[4],
max=max(Ab.units))msr %>%
mutate(label=forcats::fct_relevel(label,"s-","s+","s0"#,"s++"
)) %>%
ggplot(aes(x=Ab.units,fill=label)) +
#geom_bar(position = "fill") +
#scale_x_log10() +
geom_histogram(position = "stack") +
facet_grid(pheno~igg,scales = "free") +
labs(title="Proportion of seropositives")
5.4 Correlation
5.4.1 IgG subtypes
mco %>% dim()[1] 232 16u <- mco %>% filter(pheno=="asymptomatic") %>%
dplyr::select(ID,igg,Ab.units) %>%
#mutate(Ab.units=log10(Ab.units)) %>%
spread(igg,Ab.units) %>%
dplyr::select(-ID) %>%
mutate(pheno="asymptomatic") %>%
mutate(pheno=forcats::fct_recode(pheno,
"Asymptomatic"="asymptomatic",
"Symptomatic"="symptomatic")) %>%
dplyr::rename("IgG"="igg","IgG1"="igg1",
"IgG2"="igg2","IgG3"="igg3","IgG4"="igg4")
v <- mco %>% filter(pheno=="symptomatic") %>%
dplyr::select(ID,igg,Ab.units) %>%
#mutate(Ab.units=log10(Ab.units)) %>%
spread(igg,Ab.units) %>%
dplyr::select(-ID) %>%
mutate(pheno="symptomatic") %>%
mutate(pheno=forcats::fct_recode(pheno,
"Asymptomatic"="asymptomatic",
"Symptomatic"="symptomatic")) %>%
dplyr::rename("IgG"="igg","IgG1"="igg1",
"IgG2"="igg2","IgG3"="igg3","IgG4"="igg4")
#par(mfrow=c(1,3))
a <- union(u,v) %>%
dplyr::select(-pheno)
a %>%
PerformanceAnalytics::chart.Correlation(method = "spearman",main="all",histogram = F)
b <- union(u,v) %>%
filter(pheno=="Asymptomatic") %>%
dplyr::select(-pheno)
b %>%
PerformanceAnalytics::chart.Correlation(method = "spearman",main="asymptomatic",histogram = F)
c <- union(u,v) %>%
filter(pheno=="Symptomatic") %>%
dplyr::select(-pheno)
c %>%
PerformanceAnalytics::chart.Correlation(method = "spearman",main="symptomatic",histogram = F)
#a
#par(mfrow=c(1,1))#d <- c
#d <- b
#d <- c
#cor(d[which(complete.cases(d)),],method = "spearman")
cor.ext <- function(d) {#d <- a
e <- Hmisc::rcorr(as.matrix(d[which(complete.cases(d)),]), type="spearman")
#e
rr <- e$r %>% as.data.frame() %>% mutate_all(funs(if_else(.==1,NA_real_,.))) %>%
gather(ig,vl) %>% mutate(vl=format(vl,digits = 2)) %>% mutate(vl=as.numeric(vl)) %>%
group_by(ig) %>%
mutate(id=1:n()) %>%
spread(ig,vl) %>% dplyr::select(-id) %>% rownames_to_column() #%>% dplyr::select(-rowname)
pp <- e$P %>% as.data.frame() %>% mutate_all(funs(if_else(.<0.001,111,
if_else(.<0.01,11,
if_else(.<0.05,1,0))))) %>%
mutate_all(as.character) %>%
mutate_all(funs(if_else(.=="111","(***)",
if_else(.=="11","(**)",
if_else(.=="1","(*)","(ns)"))))) %>% rownames_to_column()
ee <- rr %>% gather(ig,vl) %>% mutate(vl="") %>% group_by(ig) %>% mutate(id=1:n()) %>% spread(ig,vl) %>% dplyr::select(-id) %>% dplyr::select(rowname,everything())
ppp <- as.matrix(pp)
rrr <- as.matrix(rr)
eee <- as.matrix(ee)
#eee <- rrr
eee[1,3] <- paste(rrr[1,3],ppp[1,3])
eee[1:2,4] <- paste(rrr[1:2,4],ppp[1:2,4])
#eee[2,4] <- paste(rrr[2,4],ppp[2,4])
eee[1:3,5] <- paste(rrr[1:3,5],ppp[1:3,5])
#eee[2,5] <- paste(rrr[2,5],ppp[2,5])
#eee[3,5] <- paste(rrr[3,5],ppp[3,5])
eee[1:4,6] <- paste(rrr[1:4,6],ppp[1:4,6])
ext <- eee %>% as_data_frame() %>% mutate(rowname=colnames(.)[-1]) #%>% #as.matrix()
#dplyr::rename("subtype"=rowname)
return(ext)
}
aa <- cor.ext(a) %>% #dplyr::rename("all"=rowname) %>%
column_to_rownames() %>% as.matrix() #%>% knitr::kable(format = "latex")
bb <- cor.ext(b) %>% #dplyr::rename("asympt"=rowname) %>%
column_to_rownames() %>% as.matrix()
cc <- cor.ext(c) %>% #dplyr::rename("sympt"=rowname) %>%
column_to_rownames() %>% as.matrix()
readr::write_rds(aa,"data/cor_all.rds")
readr::write_rds(bb,"data/cor_asymp.rds")
readr::write_rds(cc,"data/cor_sympt.rds")aa IgG IgG1 IgG2 IgG3 IgG4
IgG "" "0.53 (***)" "0.45 (**)" "0.54 (***)" "-0.12 (ns)"
IgG1 "" "" "0.24 (ns)" "0.47 (**)" " 0.16 (ns)"
IgG2 "" "" "" "0.69 (***)" " 0.25 (ns)"
IgG3 "" "" "" "" " 0.38 (*)"
IgG4 "" "" "" "" "" bb IgG IgG1 IgG2 IgG3 IgG4
IgG "" "0.548 (**)" "0.307 (ns)" "0.618 (**)" "-0.073 (ns)"
IgG1 "" "" "0.062 (ns)" "0.643 (***)" " 0.336 (ns)"
IgG2 "" "" "" "0.413 (*)" " 0.103 (ns)"
IgG3 "" "" "" "" " 0.277 (ns)"
IgG4 "" "" "" "" "" cc IgG IgG1 IgG2 IgG3 IgG4
IgG "" "0.52 (*)" "0.77 (***)" "0.68 (***)" "0.33 (ns)"
IgG1 "" "" "0.34 (ns)" "0.36 (ns)" "0.22 (ns)"
IgG2 "" "" "" "0.90 (***)" "0.57 (**)"
IgG3 "" "" "" "" "0.50 (*)"
IgG4 "" "" "" "" "" #knitr::kable(cc,"latex")ax <- t(as.matrix(c("","","","","")))
rownames(ax) <- "All individuals"
colnames(ax) <- c("IgG","IgG1","IgG2","IgG3","IgG4")
bx <- t(as.matrix(c("","","","","")))
rownames(bx) <- "Asymptomatics"
colnames(bx) <- c("IgG","IgG1","IgG2","IgG3","IgG4")
cx <- t(as.matrix(c("","","","","")))
rownames(cx) <- "Symptomatics"
colnames(cx) <- c("IgG","IgG1","IgG2","IgG3","IgG4")
dd <- rbind(ax,aa,bx,bb,cx,cc)[-c(6,12,18),-1]
readr::write_rds(dd,"data/cor_full.rds")#mco %>%
# dplyr::select(pheno,igg,Ab.units,par) %>%
# GGally::ggpairs(mapping = aes(color = pheno))
union(u,v) %>%
dplyr::rename("Status"=pheno) %>%
GGally::ggscatmat(color = "Status",corMethod = "spearman") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title="Correlation between IgG subtypes per phenotype")# +scale_fill_grey(start = 0, end = .9)
# correlación general y estratificada
#union(u,v) %>%
# GGally::ggpairs(mapping = aes(color = pheno))5.4.2 parasitemia
mco %>%
ggplot(aes(Ab.units,fill=pheno)) +
geom_density(position = "identity",alpha=0.6) +
#geom_rug() +
scale_x_log10()
mco %>% #summary(mco$par)
ggplot(aes(par,fill=pheno)) +
geom_density(position = "identity",alpha=0.6) +
#geom_rug() +
scale_x_log10()
mco %>%
ggplot(aes(Ab.units,par,fill=pheno)) +
geom_point(aes(colour=pheno)) +
scale_x_log10() +
scale_y_log10() +
facet_grid(pheno~igg,scales = "free") +
geom_smooth(aes(colour=pheno),method = lm)
u <- mco %>% filter(pheno=="asymptomatic") %>%
dplyr::select(ID,igg,Ab.units,par) %>%
#mutate(Ab.units=log10(Ab.units)) %>%
spread(igg,Ab.units) %>%
dplyr::select(-ID)
v <- mco %>% filter(pheno=="symptomatic") %>%
dplyr::select(ID,igg,Ab.units,par) %>%
#mutate(Ab.units=log10(Ab.units)) %>%
spread(igg,Ab.units) %>%
dplyr::select(-ID)
#union(u,v) %>%
# PerformanceAnalytics::chart.Correlation(histogram = FALSE,method = "spearman")mco %>%
ggplot(aes(Ab.units,par)) +
geom_point(aes(colour=pheno)) +
scale_x_log10() +
scale_y_log10() +
facet_grid(pheno~igg
#,scales = "free"
) +
geom_smooth(method = lm)f <- mco %>% mutate(igg=as.factor(igg)) %>% .$igg
g <- mco %>% mutate(pheno=as.factor(pheno)) %>% .$pheno
#mco <- mco %>%
# #dplyr::select(Ab.units,par) %>%
# mutate(Ab.units=log10(Ab.units),par=log10(par)) %>%
# filter(par!=-Inf) #%>%
stt <- data_frame(estimate=as.double(),
##estimate1=as.double(),
##estimate2=as.double(),
statistic=as.double(),
p.value=as.double(),
parameter=as.double(),#
conf.low=as.double(),#
conf.high=as.double(),#
method=as.factor(NULL),
alternative=as.factor(NULL),
igg=as.character(),
pheno=as.character(),
signf=as.character(),
rho=as.character(),
lab=as.character())
for (l in 1:length(levels(f))) {
for (k in 1:length(levels(g))) {
## Primero,
j <- mco %>% filter(par!=0) %>% filter(igg==levels(f)[l] & pheno==levels(g)[k]) %>% #l #k
#t.test(Ab.units_log ~ pheno, data = ., var.equal= i$p.value>0.05) %>%
#wilcox.test(Ab.units_log ~ pheno, data = .) %>%#,
#conf.int=TRUE) %>%
cor.test(~ log10(Ab.units) + log10(par),
data = ., method = "pearson") %>%
broom::tidy() %>% #%>% format(digits=2)
#dplyr::select(-starts_with("estimate")) %>%
mutate(igg=levels(f)[l],#l
pheno=levels(g)[k],#k
signf=if_else(p.value<0.001,"ooo",
if_else(p.value<0.01,"oo",
if_else(p.value<0.05,"o","ns"#""#
))),
rho= estimate %>% format(digits=2),
lab= paste0(#"t=",#should be done -> mix variance equality test
#"W=",#non-parametric used in literature
#"S=",f$statistic,", rho="
"rho=",estimate %>% format(digits=2),
"\nP",ifelse(p.value<0.001,"<0.001",
paste0("=",p.value %>% format(digits=2)))
)
)
#i <- mco %>% filter(igg==levels(f)[l] & pheno==levels(g)[k]) %>% #l #k
# #t.test(Ab.units_log ~ pheno, data = ., var.equal= i$p.value>0.05) %>%
# #wilcox.test(Ab.units_log ~ pheno, data = .) %>%#,
# #conf.int=TRUE) %>%
# cor.test(~ Ab.units + par,
# data = ., method = "spearman") %>%
# broom::tidy() %>% #%>% format(digits=2)
# #dplyr::select(-starts_with("estimate")) %>%
# mutate(igg=levels(f)[l],#l
# pheno=levels(g)[k],#k
# signf=if_else(p.value<0.001,"(***)",
# if_else(p.value<0.01,"(**)",
# if_else(p.value<0.05,"(*)","(ns)"))),
# rho= estimate %>% format(digits=2),
# lab= paste0(#"t=",#should be done -> mix variance equality test
# #"W=",#non-parametric used in literature
# #"S=",f$statistic,", rho="
# "rho=",estimate %>% format(digits=2)#,
# #"\nP",ifelse(p.value<0.001,"<0.001",
# # paste0("=",p.value %>% format(digits=2)))
# )
# )
stt <- stt %>% union(j) #%>% union(i)
}
}
sttt <- stt %>% arrange(igg,pheno) %>% dplyr::select(igg,pheno,everything()) %>%
mutate(x=if_else(igg=="igg1"|igg=="igg3"|igg=="igg4",10,
if_else(igg=="igg2",.7,.1)),
y=60000)
# plot
specie_name <- c("asymptomatic"="Asymptomatic","symptomatic"="Symptomatic",
"igg"="IgG","igg1"="IgG1","igg2"="IgG2","igg3"="IgG3","igg4"="IgG4"
)
mco %>%
filter(par!=0) %>%
ggplot(aes(Ab.units,par)) +
geom_point(#aes(colour=pheno)
) +
scale_x_log10() +
scale_y_log10(limits=c(100,65000)
) +
facet_grid(pheno~igg,labeller = as_labeller(specie_name)
,scales = "free_x"
) +
#geom_smooth(method = lm,se=F,col="grey50",lwd=.5) +
#geom_smooth(method = loess,se=F,col="grey50",lwd=.5) +
geom_text(aes(x,y,label=paste0("r","==",
rho,"^",signf
)),data = sttt, parse = T,
vjust=1,hjust=0.05,size=3.5) +
#labs(#title="Correlation between AU and parasitemia per IgG subtype and phenotype",
# caption="oo: P < 0.01; o: P < 0.05; ns: non-significant"
# ) +
xlab("Antibody units (log scale)") +
ylab(expression(paste("Parasite/",mu,"L"," (log scale)"))) #+ #ylab("Parasite/uL") +
#coord_cartesian(ylim = c(0,20000))
#scale_color_grey(#start = 0, end = .9,
# name="Carrier",labels=c("Asymptomatic","Symptomatic"#,"Indeterminate"
# ))d <- data.frame(x=1:3,y=1:3)
qplot(x, y, data=d) + geom_text(aes(2, 2.5,
label="rho~and~some~other~text"), parse=TRUE)
f <- mco %>% mutate(igg=as.factor(igg)) %>% .$igg
#mco <- mco %>%
# #dplyr::select(Ab.units,par) %>%
# mutate(Ab.units=log10(Ab.units),par=log10(par)) %>%
# filter(par!=-Inf) #%>%
stt <- data_frame(estimate=as.double(),
##estimate1=as.double(),
##estimate2=as.double(),
statistic=as.double(),
p.value=as.double(),
#parameter=as.double(),#
#conf.low=as.double(),#
#conf.high=as.double(),#
method=as.factor(NULL),
alternative=as.factor(NULL),
igg=as.character(),
lab=as.character())
for (l in 1:length(levels(f))) {
## Primero,
j <- mco %>% filter(igg==levels(f)[l]) %>% #l
#t.test(Ab.units_log ~ pheno, data = ., var.equal= i$p.value>0.05) %>%
#wilcox.test(Ab.units_log ~ pheno, data = .) %>%#,
#conf.int=TRUE) %>%
cor.test(~ Ab.units + par,
data = ., method = "spearman") %>%
broom::tidy() %>% #%>% format(digits=2)
#dplyr::select(-starts_with("estimate")) %>%
mutate(igg=levels(f)[l],#l
lab= paste0(#"t=",#should be done -> mix variance equality test
#"W=",#non-parametric used in literature
#"S=",f$statistic,", rho="
"rho=",estimate %>% format(digits=2),
"\nP",ifelse(p.value<0.001,"<0.001",
paste0("=",p.value %>% format(digits=2)))
)
)
stt <- stt %>% union(j)
}
stt <- stt %>% arrange(igg) %>% dplyr::select(igg,everything()) %>%
mutate(x=.1,
y=200)
# plot
mco %>% #filter(igg==levels(f)[1]) %>%
ggplot(aes(Ab.units,par)) +
geom_point() +
scale_x_log10() +
scale_y_log10() +
geom_smooth(method = lm,se=F,col="grey50",lwd=.5) +
#geom_smooth(method = loess,se=F,col="grey50",lwd=.5) +
geom_text(aes(x,y,label=lab),data = stt,
vjust=.5,hjust=0.05,size=3.5) +
facet_grid(~igg
#,scales = "free"
) +
#facet_wrap(~igg,ncol = 5)
labs(title="Correlation between AU and parasitemia per IgG subtype")
5.4.3 age
age <- mco %>%
dplyr::select(ID, code, pheno, igg, Ab.units) %>%
full_join(mcv,by=c("ID","pheno"))age %>% #summary(age$edad)
ggplot(aes(Ab.units,fill=pheno)) +
geom_density(position = "identity",alpha=0.7) +
facet_wrap(~igg,scales = "free",ncol = 5) #+scale_x_log10()
age %>% #summary(age$edad)
ggplot(aes(edad,fill=pheno)) +
geom_density(position = "identity",alpha=0.7)
f <- age %>% mutate(igg=as.factor(igg)) %>% .$igg
g <- age %>% mutate(pheno=as.factor(pheno)) %>% .$pheno
#mco <- mco %>%
# #dplyr::select(Ab.units,par) %>%
# mutate(Ab.units=log10(Ab.units),par=log10(par)) %>%
# filter(par!=-Inf) #%>%
stt <- data_frame(estimate=as.double(),
##estimate1=as.double(),
##estimate2=as.double(),
statistic=as.double(),
p.value=as.double(),
#parameter=as.double(),#
#conf.low=as.double(),#
#conf.high=as.double(),#
method=as.factor(NULL),
alternative=as.factor(NULL),
igg=as.character(),
pheno=as.character(),
signf=as.character(),
rho=as.character(),
lab=as.character())
for (l in 1:length(levels(f))) {
for (k in 1:length(levels(g))) {
## Primero,
j <- age %>% filter(igg==levels(f)[l] & pheno==levels(g)[k]) %>% #l #k
#t.test(Ab.units_log ~ pheno, data = ., var.equal= i$p.value>0.05) %>%
#wilcox.test(Ab.units_log ~ pheno, data = .) %>%#,
#conf.int=TRUE) %>%
cor.test(~ Ab.units + edad,
data = ., method = "spearman") %>%
broom::tidy() %>% #%>% format(digits=2)
#dplyr::select(-starts_with("estimate")) %>%
mutate(igg=levels(f)[l],#l
pheno=levels(g)[k],#k
signf=if_else(p.value<0.001,"ooo",
if_else(p.value<0.01,"oo",
if_else(p.value<0.05,"o","ns"#""#
))),
rho= estimate %>% format(digits=2),
lab= paste0(#"t=",#should be done -> mix variance equality test
#"W=",#non-parametric used in literature
#"S=",f$statistic,", rho="
"rho=",estimate %>% format(digits=2),
"\nP",ifelse(p.value<0.001,"<0.001",
paste0("=",p.value %>% format(digits=2)))
)
)
#i <- age %>% filter(igg==levels(f)[l] & pheno==levels(g)[k]) %>% #l #k
# #t.test(Ab.units_log ~ pheno, data = ., var.equal= i$p.value>0.05) %>%
# #wilcox.test(Ab.units_log ~ pheno, data = .) %>%#,
# #conf.int=TRUE) %>%
# cor.test(~ Ab.units + edad,
# data = ., method = "spearman") %>%
# broom::tidy() %>% #%>% format(digits=2)
# #dplyr::select(-starts_with("estimate")) %>%
# mutate(igg=levels(f)[l],#l
# pheno=levels(g)[k],#k
# lab= paste0(#"t=",#should be done -> mix variance equality test
# #"W=",#non-parametric used in literature
# #"S=",f$statistic,", rho="
# "rho=",estimate %>% format(digits=2),
# "\nP",ifelse(p.value<0.001,"<0.001",
# paste0("=",p.value %>% format(digits=2)))
# )
# )
stt <- stt %>% union(j) #%>% union(i)
}
}
sttu <- stt %>% arrange(igg,pheno) %>% dplyr::select(igg,pheno,everything()) %>%
mutate(x=.1,
y=78)
# plot
specie_name <- c("asymptomatic"="Asymptomatic","symptomatic"="Symptomatic",
"igg"="IgG","igg1"="IgG1","igg2"="IgG2","igg3"="IgG3","igg4"="IgG4"
)
age %>%
ggplot(aes(Ab.units,edad)) +
geom_point(#aes(colour=pheno)
) +
#scale_x_log10() +
#scale_y_log10() +
facet_grid(pheno~igg,labeller = as_labeller(specie_name)
,scales = "free_x"
) +
#geom_smooth(method = lm,se=F,col="grey50",lwd=.5) +
#geom_smooth(method = loess,se=F,col="grey50",lwd=.5) +
geom_text(aes(x,y,label=paste0("rho","==",
rho,"^",signf
)),data = sttu, parse = T,
vjust=.5,hjust=0.05,size=3.5) +
#labs(title="Correlation between AU and parasitemia per IgG subtype and phenotype") +
xlab("Antibody units") + ylab("Age (years)") +
coord_cartesian(ylim = c(0,85))
#scale_color_grey(#start = 0, end = .9,
# name="Carrier",labels=c("Asymptomatic","Symptomatic"#,"Indeterminate"
# ))f <- age %>% mutate(igg=as.factor(igg)) %>% .$igg
#mco <- mco %>%
# #dplyr::select(Ab.units,par) %>%
# mutate(Ab.units=log10(Ab.units),par=log10(par)) %>%
# filter(par!=-Inf) #%>%
stt <- data_frame(estimate=as.double(),
##estimate1=as.double(),
##estimate2=as.double(),
statistic=as.double(),
p.value=as.double(),
#parameter=as.double(),#
#conf.low=as.double(),#
#conf.high=as.double(),#
method=as.factor(NULL),
alternative=as.factor(NULL),
igg=as.character(),
lab=as.character())
for (l in 1:length(levels(f))) {
## Primero,
j <- age %>% filter(igg==levels(f)[l]) %>% #l
#t.test(Ab.units_log ~ pheno, data = ., var.equal= i$p.value>0.05) %>%
#wilcox.test(Ab.units_log ~ pheno, data = .) %>%#,
#conf.int=TRUE) %>%
cor.test(~ Ab.units + edad,
data = ., method = "spearman") %>%
broom::tidy() %>% #%>% format(digits=2)
#dplyr::select(-starts_with("estimate")) %>%
mutate(igg=levels(f)[l],#l
lab= paste0(#"t=",#should be done -> mix variance equality test
#"W=",#non-parametric used in literature
#"S=",f$statistic,", rho="
"rho=",estimate %>% format(digits=2),
"\nP",ifelse(p.value<0.001,"<0.001",
paste0("=",p.value %>% format(digits=2)))
)
)
stt <- stt %>% union(j)
}
stt <- stt %>% arrange(igg) %>% dplyr::select(igg,everything()) %>%
mutate(x=.1,
y=55)
# plot
age %>% #filter(igg==levels(f)[1]) %>%
ggplot(aes(Ab.units,edad)) +
geom_point() +
scale_x_log10() +
#scale_y_log10() +
geom_smooth(method = lm,se=F,col="grey50",lwd=.5) +
#geom_smooth(method = loess,se=F,col="grey50",lwd=.5) +
geom_text(aes(x,y,label=lab),data = stt,
vjust=.5,hjust=0.05,size=3.5) +
facet_grid(~igg
#,scales = "free"
) +
#facet_wrap(~igg,ncol = 5)
labs(title="Correlation between AU and parasitemia per IgG subtype")
5.5 Association
- outcome: symptomatic P.f. malaria
5.5.1 On Power and sample size
5.5.1.1 minimum number of candidate predictors
case=20
ctrl=24
m_c=min(case,ctrl) #limiting sample size
p=5 #current number of predictors
#round(m_c/15) #
p<m_c/15[1] FALSE- Given the current limiting sample size, for a reliable model the number of candidate predictors must be lower than 1.
5.5.1.2 minimum number of cases
k=5 #number of covariates
p= 0.4 # prevalence of outcome
n_x=10*k/p- Given the measured covariates and prevalence of modeled outcome, the minimum sample size required is 125.
# exposition: symptomatic malaria (60% asympt in Pf)
#
# given the proportion (prevalence) relevant OR and required power,
# obtain the sample size required
p=0.40 #proportion of exposure in general population OR outcome provalence
#(OR=pA*(1-pB)/pB/(1-pA)) # 2
OR=.5 #hypothetical OR
#
kappa=1 # sampling ratio between case:control
alpha=0.05 #type 1 error (false positives)
beta=0.20 #power=1-beta #type 2 error (false negatives)
#(n= (((1+kappa)^2)*((qnorm(1-alpha/2)+qnorm(1-beta))^2)) / (kappa*p*(1-p)*((log(OR))^2)) )
n1= (4*((qnorm(1-alpha/2)+qnorm(1-beta))^2)) / (p*(1-p)*((log(OR))^2))
#ceiling(n) # Afridi 220, Bragga 263, Stanisic 206, Medeiros 28+24- Given the known prevalence of symptomatic malaria in Iquitos and a relevant Odds Ratio reported in literature, a sample size of 273 would be required to achieve a power 80%.
# given the available sample size and obtained Odds Ratio,
# obtain the actual power
n2= 44
#(OR=.5)
z=log(OR)*sqrt(n2)/sqrt(((1+kappa)^2)/(kappa*p*(1-p)))
Power=pnorm(z-qnorm(1-alpha/2))+pnorm(-z-qnorm(1-alpha/2))- Following the same method, given the current sample size, this analysis have a power of 20.32%.
5.5.2 Tidy up
5.5.2.1 inmuno
#library(tidyverse)
mmo <- mco %>% #filter(igg=="igg") %>%
mutate(pheno=as.factor(pheno)) %>%
mutate(pheno=forcats::fct_relevel(pheno,"symptomatic")) %>%
mutate(pheno.num=ifelse(pheno=="asymptomatic",0,1)) %>%
mutate(pheno.log=ifelse(pheno=="asymptomatic",FALSE,TRUE))- the only reads available for sample
2235are for the asymptomatic state!
cova %>%
covb %>%
covx %>%
filter(codigo=="2235" | codigo=="3053" | codigo=="9165" | codigo=="9801") %>%
arrange(codigo) #%>%
#dplyr::select(codigo,edad,sexo,par,everything())## add covariates
full_join(mmo,mcv,by=c("ID","par","pheno")) %>%
filter(ID=="2235" | ID=="3053" | ID=="9165" | ID=="9801") %>%
arrange(ID)5.5.2.2 covariates
- NOTA: 2235 tiene dos observaciones en matriz de covariables, pero en templates solo ha sido identificado como asintomático!
mcv_t <- mcv %>% #filter(igg=="igg") %>%
mutate(pheno=as.factor(pheno)) %>%
mutate(pheno=forcats::fct_relevel(pheno,"symptomatic")) %>%
mutate(pheno.num=ifelse(pheno=="asymptomatic",0,1)) %>%
mutate(pheno.log=ifelse(pheno=="asymptomatic",FALSE,TRUE))5.5.2.3 inmuno per subclass
### MULTIPLE LOGISTIC REGRESSION
mmo_x <- mmo %>%
dplyr::select(ID,
#code,
#pheno,
igg
,Ab.units
#,Ab.units_log
,pheno.num#,pheno.log # 1: symptomatic, 0:: asymptomatic
) %>%
spread(igg
,Ab.units
#,Ab.units_log
)
#mmo_x.omit = na.omit(mmo_x)5.5.2.4 add covariates
mlr <- mcv_t %>%
dplyr::select(ID,pheno.num, edad, sexo, par) %>%
inner_join(mmo_x,by=c("ID","pheno.num"))
mlr_na = na.omit(mlr)
#dd <- rms::datadist(mlr_na); options(datadist='dd')# https://stackoverflow.com/questions/7505547/detach-all-packages-while-working-in-r
detachAllPackages <- function() {
basic.packages <- c("package:stats",
"package:graphics",
"package:grDevices",
"package:utils",
"package:datasets",
"package:methods",
"package:base")
package.list <- search()[
ifelse(
unlist(
gregexpr("package:",search())
)==1,TRUE,FALSE
)
]
package.list <- setdiff(package.list,basic.packages)
if (length(package.list)>0)
for (package in package.list)
detach(package, character.only=TRUE)
}
detachAllPackages()# EXPLAINED: https://stats.stackexchange.com/questions/64788/interpreting-a-logistic-regression-model-with-multiple-predictors
library(tidyverse)
mmo_x_rms <- mlr_na %>%
mutate(igg_c=ntile(igg,3),
igg1_c=ntile(igg1,3),
igg2_c=ntile(igg2,3),
igg3_c=ntile(igg3,3),
igg4_c=ntile(igg4,3)
) %>%
mutate(igg_c=as.factor(igg_c),
igg1_c=as.factor(igg1_c),
igg2_c=as.factor(igg2_c),
igg3_c=as.factor(igg3_c),
igg4_c=as.factor(igg4_c)) %>%
mutate(igg_d=igg/10,
igg1_d=igg1/10,
igg2_d=igg2/10,
igg3_d=igg3/10,
igg4_d=igg4/10) %>%
mutate(igg_l=log10(igg),
igg1_l=log10(igg1),
igg2_l=log10(igg2),
igg3_l=log10(igg3),
igg4_l=log10(igg4)) #%>%
#dplyr::select(ID,pheno.num,
# igg=igg_c,
# igg1=igg1_c,
# igg2=igg2_c,
# igg3=igg3_c,
# igg4=igg4_c
# )
dd <- rms::datadist(mmo_x_rms); options(datadist='dd')5.5.2.5 data with missings
mmo %>% dplyr::count(igg)5.5.3 Univariate
5.5.3.1 PRE: univariate
5.5.3.1.1 age
5.5.3.1.2 sex
5.5.3.1.3 parasitemia
5.5.3.1.4 igg
5.5.3.1.5 igg1
5.5.3.1.6 igg2
5.5.3.1.7 igg3
5.5.3.1.8 igg4
5.5.3.1.9 IgG model
5.5.3.1.10 stepwise selection
5.5.3.1.10.1 selected model
6 References
1. Miura K, Orcutt AC, Muratova OV, Miller LH, Saul A, Long CA. Development and characterization of a standardized ELISA including a reference serum on each plate to detect antibodies induced by experimental malaria vaccines. Vaccine. 2008;26(2):193-200. doi:10.1016/j.vaccine.2007.10.064.
2. Hughes S. Plater: Read, Tidy, and Display Data from Microtiter Plates.; 2016. https://CRAN.R-project.org/package=plater.
3. Wickham H. Readxl: Read Excel Files.; 2016. https://CRAN.R-project.org/package=readxl.
4. Garmonsway D. Tidyxl: Read Untidy Excel Files. https://github.com/nacnudus/tidyxl.
5. Ritz C, Baty F, Streibig JC, Gerhard D. Dose-response analysis using r. Xia Y, ed. PLOS ONE. 2015;10(12):e0146021. doi:10.1371/journal.pone.0146021.
6. Sveidqvist K, Bostock M, Pettitt C, Daines M, Kashcha A, Iannone R. DiagrammeR: Create Graph Diagrams and Flowcharts Using R.; 2016. https://CRAN.R-project.org/package=DiagrammeR.
---
title: "unap-rafael"
author: "Andree Valle Campos"
date: '`r Sys.Date()`'
output:
  html_notebook:
  #html_document:#pdf_document -> keep_tex: yes
    fig_caption: yes
    toc: yes
    number_sections: true
    toc_depth: 5
    toc_float:
      collapsed: yes
    code_folding: "hide"
    #df_print: paged
bibliography: analysis/SeroMarker.bib
biblio-style: apalike
link-citations: yes
csl: analysis/american-medical-association.csl
---

<!--

HIGHLIGTHS
- Niveles de anticuerpos de IgG total están asociados a una protección contra a la malaria sintomática.
- Niveles de anticuerpos de la subclase IgG3 presentan la mayor asociación a la presencia de malaria sintomática.
- Asintomáticos y sintomáticos presentan diferencias en el niveles y proporción de seropositivos para las subclases IgG1 e IgG3.

 y debilmente asociada con la presencia de síntomas (IQR-AOR= 0.58, 95% CI= 0.29 – 1.17)

 Sin embargo, se identificó que la subclase IgG3 estuvo una mayor asociación con la presencia de síntomas (IQR-AOR=1.61, 95% CI= 0.61 – 4.21)

MANUSCRIPT NOTES:

 Obtuvimos que pacientes asintomáticos presentan una mayor respuesta de anticuerpos IgG que pacientes sintomáticos (W= 539, p<0.05), inversamente correlacionada a la densidad de parásitos (rho= -0.42, p<0.01) y debilmente asociada con la presencia de síntomas (IQR-OR= 0.48, 95% CI= 0.19 – 1.21). Con respecto a las subclases, se observó un mayor nivel de respuesta y proporción de seropositivos en IgG1 e IgG3 sin diferencias entre sintomáticos y asintomáticos (P>0.05). Sin embargo, se identificó que la subclase IgG3 estuvo altamente asociada a la presencia de síntomas (IQR-OR=2.35, 95% CI= 0.80 – 6.89).

- We used a multiple logistic regression and estimated odd ratios (OR) for each IgG subclass to determined the association between the symptomatic profile and intensity of antibody response

We verified the association between the presence of symptomatic malaria, as a binary response, and the set of total IgG and subclasses as continuos predictors, using age and sex as adjustment covariates in the multivariate analysis. We performed a logistic regression with penalized maximum likelihood estimation

3.4. Asociación de AU y asintomatología

-->

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, #fig.path = "01-",
                      warning = FALSE)
#knitr::opts_knit$set(root.dir = '../.')

options(width = 60) # expand limits of CONSOLE output

#require(Hmisc)
#mu <- markupSpecs$html   # markupSpecs is in Hmisc
# hidden command (<code>r mu$widescreen()</code>) to use an entire wide screen. # mu$widescreen()
#`r mu$widescreen()`
```

# ELISA STANDARDIZATION {#stdrad}

## Aim

* Principal: 
    - Implement a reproducible standardization of OD values across ELISA plates following 
    **Miura et.al., 2008**[@Miura2008].

* Secondary: 
    - Generate useful outputs to compare standardization quality.


## To Do

- Write methods for the manuscript.

## Dependencies

The required R packages for this analysis are:

+ `plater` [@plater]
+ `readxl` [@readxl]
+ `tidyxl` [@tidyxl]
+ `drc` [@Ritz2015]

```{r, results='hide', message=FALSE}
##essential
library(tidyverse)    # set of tidy packages (readr, tibble, dplyr, tidyr, ggplot2, ¿purr?)
library(tidyxl)       # read untidy excel formats
library(readxl)       # read excel files as tidy tables
library(drc)          # fit dose-response models
library(mixtools)     # analyze finete mixture models
##accesory
library(DiagrammeR)   # create flowchart
```

```{r}
theme_set(theme_bw())
```


## Method

- using `DiagrammeR` [@DiagrammeR]

```{r, fig.align='center', fig.width=9}
#install.packages("DiagrammeR")
#library(DiagrammeR)

DiagrammeR("
  graph LR
    A[XLS data] -.-> |readxl+tidyxl| B{R data}
    Z[CSV data] -.-> |plater| B{R data}
    B --> C1[STD]
    B --> E[ctr +/-]
    B --> D1[UNK]
    
    C1 -.-> |drc| C2[4pLL model]
    C2 --> C3[Box-Cox]
    C3 --> F{UNK Ab.units}

    D1 --> D2[mean.OD]
    D2 --> D3[OD %CV]
    D3 --> F
    
    F --> G1[Histogram]
    F --> G2[Density]
    F --> G3[QQPlot]

    style Z fill:#ffffff, stroke:#000000, stroke-width:2px    
    style A fill:#ffffff, stroke:#000000, stroke-width:2px
    style B fill:#ffffff, stroke:#000000, stroke-width:2px
    style C1 fill:#ffffff, stroke:#000000, stroke-width:2px
    style C2 fill:#ffffff, stroke:#000000, stroke-width:2px
    style C3 fill:#ffffff, stroke:#000000, stroke-width:2px
    style D1 fill:#ffffff, stroke:#000000, stroke-width:2px
    style D2 fill:#ffffff, stroke:#000000, stroke-width:2px
    style D3 fill:#ffffff, stroke:#000000, stroke-width:2px
    style E fill:#ffffff, stroke:#000000, stroke-width:2px
    style F fill:#ffffff, stroke:#000000, stroke-width:2px
    style G1 fill:#ffffff, stroke:#000000, stroke-width:2px
    style G2 fill:#ffffff, stroke:#000000, stroke-width:2px
    style G3 fill:#ffffff, stroke:#000000, stroke-width:2px
")
```

### Log-Logistic (4pLL) model

The curve of the **log-logistic symetric** model describe the *response* `f(x)` dependent of the *dose* `x` 
and **04 parameters**: $$ f(x)=f(x;b,c,d,e)=c+\frac{d-c}{1+\exp[b(log(x)-log(e))]}\ $$ where: 
  
- `c` is the **lower limit** of the response when the *dose* `x` approaches infinity, 
- `d` is the **upper limit** when the *dose* `x` approaches zero,
- `b` is the **slope** around the **point of inflection**, represented by 
- `e` defined as **effective dose** and commmonly denoted as [@Ritz2015]:
    + `ED50`, `EC50` or `IC50` for continuous responses,
    + `LD50` or `LC50` for binomial responses, and
    + $T_{50}$ for event-time responses.

## Procedure

**12 summary plots** per ELISA Template:

- **3x3 plots** of STD and UNK distribution, residual variance distribution, and model transformation.
- **1x3 plots** of OD~450nm~, mean.OD and Ab.units distributions by Density plots.

```{r}
getwd()
```

### phenotypes

```{r}
#x <- tidyxl::tidy_xlsx("data-raw/raw/unap-tesis/RAFAEL-data/TEMPLATES Rafael.xlsx")$data$`TEMPLATE ELISA N°1`
x <- tidyxl::tidy_xlsx("data-raw/raw/unap-tesis/RAFAEL-data/TEMPLATES Rafael.xlsx")$data
#str(x)

y <- x[[3]] %>% #i
  filter(row %in% 23:29,
         col %in% 2:13) %>% 
  dplyr::select(address,row,col,numeric,character,local_format_id) %>% 
  unite(ID,c("numeric","character")) %>% 
  mutate(ID= stringr::str_replace(ID,"_NA|NA_", ""),
         Plate= paste0("N",3)) %>% #i
  dplyr::select(Plate,everything()) %>% 
  filter(!ID %in% c("C+","C-","NA","Blank")) #%>% group_by(ID) %>% slice(1) %>% ungroup()

y <- y[FALSE,]
#str(y)

for(i in 1:length(x)){
  
  a <- x[[i]] %>% #i
  filter(row %in% 23:29,
         col %in% 2:13) %>% 
  dplyr::select(address,row,col,numeric,character,local_format_id) %>% 
  unite(ID,c("numeric","character")) %>% 
  mutate(ID= stringr::str_replace(ID,"_NA|NA_", ""),
         Plate= paste0("N",i)) %>% #i
  dplyr::select(Plate,everything()) %>% 
  filter(!ID %in% c("C+","C-","NA","Blank")) #%>% group_by(ID) %>% slice(1) %>% ungroup()
  
  y <- union(y,a)
  
}

y <- y %>% arrange(Plate,row,col)
#y %>% dplyr::count(Plate)
#std.raw
```

```{r}
y <- y %>%
  #dplyr::count(ID) %>% dplyr::arrange(desc(n))
  #filter(ID==2235)
  #filter(ID==1856)
  #filter(ID==3942)
  mutate(pheno=ifelse(local_format_id %in% c(23,17,20,18,21,19,22),"asymptomatic",
                      ifelse(local_format_id %in% c(68,69,70,71,72,73,24),"symptomatic",
                             NA_character_)
                      )
         ) #%>% 
  #filter(ID==2235)
  #filter(pheno=="asymptomatic") #%>% dplyr::select(ID)
  #mutate(pheno=ifelse(ID %in% . %>% filter(pheno=="asymptomatic") %>% dplyr::select(ID),"asym","sym"))

w <- y %>% filter(pheno=="asymptomatic") %>% dplyr::select(ID)

phe <- y %>% 
  mutate(pheno= ifelse(ID %in% w$ID,"asymptomatic","symptomatic")) %>% # RESPETA pheno de PLACA 1 y 2 !
  mutate(igg=ifelse(local_format_id %in% c(85),"igg1",
                    ifelse(local_format_id %in% c(87),"igg2",
                           ifelse(local_format_id %in% c(88,25),"igg3",
                                  ifelse(local_format_id %in% c(90),"igg4",
                                         "igg")
                                  )
                           )
                    )
         ) %>% #group_by(ID,igg) %>% slice(1) %>% ungroup() %>% 
  dplyr::select(-local_format_id#,-address,-row,-col
         ) %>% 
  arrange(Plate,row,col) %>% 
  mutate(Plate= as.factor(Plate)) %>% 
  dplyr::select(-address,-starts_with("row"),-col#,-loc
         ) %>% 
  mutate(pheno=ifelse(Plate=="N2" | 
                        Plate=="N6" | 
                        Plate=="N7",
                      "symptomatic",pheno)) # RESPETAR pheno POR PLACA (OJO: más de un pheno por paciente)
  
#filter(ID=="3053") # asympt in template 6 y 7
  #dplyr::count(pheno,igg)
  #dplyr::count(igg)
  

#phe# %>% dplyr::count(Plate)
#phe %>% group_by(Plate) %>% slice(1)

#phe %>% dplyr::count(Plate,pheno,igg) %>% arrange(Plate)
```

### data

```{r,message=FALSE}
wb_sheet <- readxl::excel_sheets("data-raw/raw/unap-tesis/RAFAEL-data/TEMPLATES Rafael.xlsx")

all <- data_frame(ID=as.character(),
                  OD=as.double(),
                  Plate=as.character(),
                  Type=as.character(),
                  Ab.unit=as.double(),
                  order=as.integer()
                  )
#str(all)

# 2 GENERATE R DATA.FRAME
for (j in 1:length(wb_sheet)) {
  
  wb_Pf_main <- readxl::read_xlsx("data-raw/raw/unap-tesis/RAFAEL-data/TEMPLATES Rafael.xlsx",
                  range = "A21:M29",
                  sheet = j)#j

  wb_Pf <- readxl::read_xlsx("data-raw/raw/unap-tesis/RAFAEL-data/TEMPLATES Rafael.xlsx",
                  range = "A35:M43",
                  sheet = j)#j
  
  all_p <- wb_Pf_main %>% 
  dplyr::rename(row="X__1") %>% 
  gather(loc,ID,-row) %>% 
  mutate(loc=as.numeric(loc)) %>% 
  arrange(row,loc) %>% 
  mutate(ID= ifelse(row == "H" & loc == 9, "Blank", ID)) %>% #ANOTACION AUSCENTE EN TEMPLATES
  full_join(
    wb_Pf %>% 
      dplyr::rename(row="X__1") %>% 
      gather(loc,OD,-row) %>% 
      mutate(loc=as.numeric(loc)) %>% 
      arrange(row,loc)
  ) %>% 
  mutate(Plate=paste0("N",j),#j
         Type=ifelse(row=="A" | ID=="Blank","std",
                     ifelse(ID=="C+" | ID=="C-","ctr","unk")),
         Ab.unit=ifelse(row=="A",stringr::str_replace(ID,"STD 1/(.+)","\\1"),
                    ifelse(ID=="Blank","0",NA_character_))
         ) %>% 
  mutate(Ab.unit=as.numeric(Ab.unit)) %>% 
  mutate(Ab.unit=ifelse(row=="A",max(Ab.unit,na.rm=T)/Ab.unit,Ab.unit)) %>% #select(-row,-loc)
  replace_na(list(ID = "na")) %>% 
  filter(ID!="na") %>% 
  mutate(order=seq(1,dim(.)[1])) %>% 
  dplyr::select(#-address,
         -starts_with("row"),#-col,
         -loc)
  
  all <- union(all,all_p)
  
}

all <- all %>% arrange(Plate,order)
#all %>% dplyr::count(Type)
```

```{r}

fin <- data_frame(Plate=as.character(),
                  order=as.integer(),
                  ID=as.character(),
                  Type=as.character(),
                  Ab.unit=as.double(),
                  OD=as.double(),
                  pheno=as.character(),
                  igg=as.character()
                  )
#str(fin)

for (j in 1:length(wb_sheet)) {
  
  fin_p <- full_join(phe %>% 
            filter(Plate==levels(phe$Plate)[j]) %>% #requires j
            mutate(order=seq(13,12+dim(.)[1])),
          all %>% 
            filter(Plate==levels(phe$Plate)[j]) %>% #requires j
            filter(Type=="unk") %>% 
            dplyr::select(-Plate,-ID),
          by="order") %>% 
    dplyr::select(Plate,order,ID,Type,Ab.unit,OD,pheno,igg)
  
  fin <- union(fin,fin_p)
  
}

fin <- fin %>% arrange(Plate,order)
#fin #%>% filter(ID==3053)

#OJO!!!! MALA ANOTACIÓN
#fin %>% filter(Plate=="N2") %>% dplyr::count(pheno)
#fin %>% filter(Plate=="N2") %>% filter(pheno=="asymptomatic")
#fin %>% dplyr::count(Plate)

end <- fin %>% 
  union(all %>% 
          filter(Type!="unk") %>% 
          dplyr::select(Plate,ID,Type,Ab.unit,OD,order) %>% 
          mutate(pheno=NA_character_,
                 igg=NA_character_)
        ) %>% 
  arrange(Plate,order) %>% 
  dplyr::select(-order)

#end #%>% filter(ID==2235)
```

```{r,eval=FALSE,echo=FALSE}
#end %>% filter(Type=="unk") %>% dplyr::count(Plate,ID,pheno,igg) %>% arrange(ID,n)
#end %>% filter(Type=="unk") %>% dplyr::count(Plate,pheno,igg) #%>% arrange(ID,n)
#end %>% filter(Plate=="N5")
#end %>% filter(Plate=="N6" & pheno=="asymptomatic") %>% arrange(ID)
#end %>% filter(Plate=="N7" & pheno=="asymptomatic") %>% arrange(ID)
#end %>% filter(ID=="3053" | ID=="9165") %>% arrange(ID)
#end %>% filter(ID=="9801") %>% arrange(ID)
#end %>% filter(Plate=="N2" & pheno=="asymptomatic") %>% arrange(ID)
end %>% filter(ID=="3053" | ID=="9165" | ID=="9801") %>% arrange(ID)
```

### standarization

```{r}
# mod is mean  od (plus sd and cv)
mod <- end %>% 
  filter(Type=="unk") %>% 
  group_by(Plate,igg,ID) %>% 
  summarise_at(vars(OD),c("mean","sd")) %>% 
  ungroup() %>% 
  dplyr::rename(mean.OD="mean",
                sd.OD="sd") %>% 
  mutate(cv.OD=100*sd.OD/mean.OD) %>% 
  mutate(order=seq(1,dim(.)[1]))
  #filter(ID=="1570")

mab <- end %>% 
  filter(Type=="unk") %>% 
  group_by(Plate,igg,ID) %>% 
  slice(1) %>% 
  ungroup() %>% #filter(ID=="1570")
  mutate(order=seq(1,dim(.)[1])) %>% 
  full_join(mod %>% dplyr::select(order,mean.OD,sd.OD,cv.OD),
            by="order") %>% #mutate(test.id= ID.x==ID.y) %>% dplyr::count(test.id)
  dplyr::select(-order,-OD) %>% 
  mutate(ord=seq(1,dim(.)[1])) %>% 
  unite(code,ID,ord,sep="_",remove = F)
```

```{r,eval=FALSE, echo=FALSE}
#mab %>% dplyr::count(Plate,pheno,igg)
#mab %>% dplyr::count(Plate,ID,pheno,igg)

mab %>% filter(#ID=="2235" | 
                 ID=="3053" | ID=="9165" | ID=="9801") %>% arrange(ID)
```


#### blank issue

```{r}
#mascara
blk <- end %>% 
  filter(ID=="Blank") %>% 
  group_by(Plate) %>% slice(1) %>% ungroup() %>% 
  dplyr::select(-OD)
#media por par de blancos por placa
std <- end %>% 
  filter(ID=="Blank") %>% 
  group_by(Plate) %>% 
  summarise_at(vars(OD),mean,na.rm=T) %>% 
  ungroup() %>% 
  #dplyr::rename(mean.OD="OD") %>% 
  full_join(blk,by="Plate") %>% 
  dplyr::select(Plate,ID,Type,Ab.unit,OD,everything()) %>% 
  union(end %>% filter(Type=="std" & ID!="Blank")) %>% 
  arrange(Plate,Ab.unit) %>% 
  mutate(Plate=as.factor(Plate))
```

#### 4pll per template

```{r,message=FALSE}

mab_ir <- NULL
mod_bx <- NULL

#
# new dose levels as support for the line
#mdo$Ab.units %>% summary()
new_x <- expand.grid(exp(seq(log(0.1),log(2048),length=100)))
# db to add predictions of all plates
new <- data_frame(ord=as.character(),
                  resp=as.double(),
                  p=as.double(),
                  pmin=as.double(),
                  pmax=as.double(),
                  Plate=as.character())
#

for (j in 1:length(levels(phe$Plate))) {
  
#
# 5 PARAMETER ESTIMATION 4pLL model
#
wb.m1 <- drm(OD ~ Ab.unit, Plate, 
               data= std %>% filter(Plate==levels(phe$Plate)[j]),#j
             #data= std,
               fct = LL.4(names = c("b", "c", "d", "e")))
#
wb.model <- wb.m1
# 6 BOX-COX TRANSFORMATION against RESIDUAL heterogeneity
wb.model.BX <- boxcox(wb.model, 
                     main=expression("Optimal " ~ lambda ~ " with confidence intervals"), 
                     plotit = FALSE)
#coefficients(wb.model.BX) %>% matrix(7,4)
mab_p <- mab %>% as.data.frame()
# 7 UNK AB.UNITS ESTIMATION by INVERSE REGRESSION
mir <- ED(wb.model.BX, 
                 mab_p[mab_p$Plate==levels(phe$Plate)[j],"mean.OD"],#j
                   #wb_MEAN[1:n,5],
                   type = "absolute",interval = "delta",
                   #clevel = "Pfal", 
                 display = FALSE)
  
mab_ir <- rbind(mab_ir,mir)
mod_bx <- rbind(mod_bx,coefficients(wb.model.BX))

#
# predictions and confidence intervals
pdm <- predict(wb.model.BX, newdata = new_x, interval = "confidence")
# new data with predictions
new_p <- bind_cols(new_x %>% 
                     as.tibble() %>% 
                     rownames_to_column(var = "ord")
                   , pdm %>% 
                     as.tibble() %>% 
                     rownames_to_column(var = "ord")
                   ) %>%
  dplyr::select(-ord1) %>% 
  mutate(Plate=levels(phe$Plate)[j]) %>% 
  dplyr::rename(resp=Var1,p=Prediction,pmin=Lower,pmax=Upper)

new <- union(new,new_p)
#

}

#mab
#mab[mab$Plate==levels(phe$Plate)[1],] #%>% duplicated() %>% sum()

# 7.1 FEED UNK AB.UNITS DATA.FRAME
mdo <- mab_ir %>% as.data.frame() %>% rownames_to_column() %>% 
  #dplyr::rename(ord=rowname) %>% 
  separate(rowname,c("par","Plate","mean.OD.c"),sep = ":") %>% 
  rownames_to_column("ord") %>% 
  #mutate(ord=seq(1,dim(.)[1])) %>% 
  full_join(mab %>% 
              mutate(ord=as.character(ord)) %>% 
              mutate(mean.OD.c=as.character(mean.OD))
            ,
            by = c("ord","Plate","mean.OD.c")) %>% 
  dplyr::select(Plate,ord,ID,code,Type,pheno,igg,mean.OD,sd.OD,cv.OD,Ab.units=Estimate,
         everything(),-par,-mean.OD.c,-Ab.unit) %>% 
  filter(!code=="2235_137") # MANUAL FILTERING of replicate on different templates

mod_bt <- mod_bx %>% as.data.frame() %>% rownames_to_column() %>% as.tibble() %>% 
  dplyr::rename(Plate=rowname) %>% 
  mutate(Plate=stringr::str_replace(Plate,"(\\d)","N\\1"))

new <- new %>% mutate(ord=as.numeric(ord)) %>% arrange(Plate,ord)
```

#### outputs

```{r}
#
#fin
#end
#mod
#mab

# standard curve data
std

# estimated ab unit data
mdo

# estimated parameters per standard curve
mod_bt

# predicted model per standard curve 
new
```

```{r,eval=FALSE,echo=FALSE}
mdo %>% arrange(ID,Plate)
mdo %>% group_by(ID) %>%  dplyr::count() %>% arrange(desc(n))
mdo %>% filter(ID=="3053" | ID=="9165" | ID=="9801") %>% arrange(ID)
#mdo %>% filter(ID=="2235" | ID=="1524" | ID=="1843") %>% arrange(desc(ID))
end %>% filter(ID=="2235") %>% group_by(Plate) %>% dplyr::count(pheno,igg) #TIENE REPLICA EN OTRO TEMPLATE!!
mdo %>% filter(ID=="2235") # data filtered
```

### quality control plots

```{r}
#std
ctr <- end %>% filter(Type=="ctr")
#ctr %>% filter(Plate==levels(phe$Plate)[1] & ID=="C+") %>% .$OD
#std %>% filter(Plate==levels(phe$Plate)[1] & ID=="Blank") %>% .$OD
```

#### cv

```{r, fig.height=4, fig.width=8}
mdo %>% 
  ggplot(aes(mean.OD,cv.OD,colour=Plate)) +
  geom_point() +
  coord_cartesian(ylim = c(0,100)) +
  geom_hline(aes(yintercept=20),linetype="dashed",size=0.3) +
  geom_vline(aes(xintercept=0.25),linetype="dashed",size=0.3) +
  facet_wrap(~Plate,ncol = 4) +
  labs(title="QC plot: Intra-plate coefficient of variation")
```

#### 4pll

```{r, fig.height=4, fig.width=8}
std %>% 
  mutate(Ab.unit=ifelse(Ab.unit==0,0.5,Ab.unit)) %>% 
  ggplot(aes(Ab.unit,OD)) +
  geom_hline(aes(yintercept=OD,linetype=ID),data=ctr) +
  geom_point(aes(colour=Plate)) +
  geom_ribbon(data=new, aes(x=resp, y=p, ymin=pmin, ymax=pmax), alpha=0.2) +
  geom_line(data=new, aes(x=resp, y=p, colour=Plate)) +
  #coord_trans(x="log") +
  scale_x_log10() +
  #theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  facet_wrap(~Plate,ncol = 4) +
  labs(title="QC plot: 4-parameter log-logistic model per plate")
  #xlab("Ferulic acid (mM)") + ylab("Root length (cm)")
```

```{r,eval=FALSE}
std %>% 
  ggplot(aes(Ab.unit,OD,colour=Plate)) +
  geom_point() +
  facet_wrap(~Plate)
```

```{r, fig.height=5, fig.width=6, eval=FALSE}
std %>% 
  ggplot(aes(log10(Ab.unit),OD,colour=Plate)) +
  geom_hline(aes(yintercept=OD,linetype=ID),ctr,size=0.3) +
  geom_point() +
  facet_wrap(~Plate)
```

```{r, fig.height=4, fig.width=8}
mdo %>% 
  ggplot(aes(Ab.units,mean.OD,colour=igg)) +
  coord_cartesian(ylim = c(0,1)) +
  geom_point() +
  geom_errorbarh(aes(xmin=Lower,xmax=Upper), colour="black", size=.2) +
  scale_x_log10() +
  facet_wrap(~Plate+pheno,nrow = 2) + 
  labs(title="Estimates of antibody units (AU) per plate and phenotype")
  # inverse regression method
  #facet_wrap(igg~pheno,nrow = 2)
```

```{r, fig.height=4, fig.width=8,eval=FALSE,echo=FALSE}
#### se
mdo %>% 
  mutate(std_error=Upper-Lower) %>% 
  ggplot2::ggplot(aes(std_error,Ab.units, colour=Plate)) +
  geom_point(alpha=0.5) +
  #geom_histogram(#binwidth = 100,
                 #breaks=seq(0, 3500, by = 100)
                 #position = "fill"
                 #) +
  geom_vline(xintercept = 1000, #lwd=2,#col = "red", 
             lty=3) +
  facet_wrap(~Plate+pheno
             ,nrow = 2#,scales = "free"
             ) + 
  #scale_x_log10() +
  #scale_y_log10() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title="Standard errors of antibody units (AU) per plate and phenotype")
```

### distribution plots

#### linear

##### scale fix

```{r,fig.width=10,fig.height=4}
##### ab units
a <- mdo %>% 
  ggplot(aes(x=Ab.units,fill=pheno)) + theme_bw() #+
  #scale_x_log10()

b <- a +
  geom_histogram(alpha=.5,position = "identity") + facet_grid(~igg)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/ab_hist.png")

c <- a + 
  geom_density(alpha=.5,position = "identity") + facet_grid(~igg)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/ab_dens.png")
  #facet_wrap(transform~measure,scales = "free")

Rmisc::multiplot(b,c,cols = 1)
```


##### scale free

```{r,fig.width=10,fig.height=4}
##### ab units
a <- mdo %>% 
  ggplot(aes(x=Ab.units,fill=pheno)) + theme_bw() #+
  #scale_x_log10()

b <- a +
  geom_histogram(alpha=.5,position = "identity") + 
  facet_wrap(~igg, scale= "free", ncol = 5)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/ab_hist.png")

c <- a + 
  geom_density(alpha=.5,position = "identity") + 
  facet_wrap(~igg, scale= "free", ncol = 5)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/ab_dens.png")
  #facet_wrap(transform~measure,scales = "free")

Rmisc::multiplot(b,c,cols = 1)
```


```{r,fig.width=10,fig.height=4,eval=FALSE}
##### od
a <- mdo %>% 
  ggplot(aes(x=mean.OD,fill=pheno)) + theme_bw() #+
  #scale_x_log10()

b <- a +
  geom_histogram(alpha=.5,position = "identity") + facet_grid(~igg)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/od_hist.png")

c <- a + 
  geom_density(alpha=.5,position = "identity") + facet_grid(~igg)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/od_dens.png")
  #facet_wrap(transform~measure,scales = "free")

Rmisc::multiplot(b,c,cols = 1)
```

#### log

##### scale fix

```{r,fig.width=10,fig.height=4}
##### ab units
a <- mdo %>% 
  ggplot(aes(x=Ab.units,fill=pheno)) + theme_bw() +
  scale_x_log10()

b <- a +
  geom_histogram(alpha=.5,position = "identity") + facet_grid(~igg)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/ab_hist.png")

c <- a + 
  geom_density(alpha=.5,position = "identity") + facet_grid(~igg)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/ab_dens.png")
  #facet_wrap(transform~measure,scales = "free")

Rmisc::multiplot(b,c,cols = 1)
```

##### scale free

```{r,fig.width=10,fig.height=4}
##### ab units
a <- mdo %>% 
  ggplot(aes(x=Ab.units,fill=pheno)) + theme_bw() +
  scale_x_log10()

b <- a +
  geom_histogram(alpha=.5,position = "identity") + 
  facet_wrap(~igg, scale= "free", ncol = 5)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/ab_hist.png")

c <- a + 
  geom_density(alpha=.5,position = "identity") + 
  facet_wrap(~igg, scale= "free", ncol = 5)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/ab_dens.png")
  #facet_wrap(transform~measure,scales = "free")

Rmisc::multiplot(b,c,cols = 1)
```

```{r,fig.width=10,fig.height=4,eval=FALSE}
##### od
a <- mdo %>% 
  ggplot(aes(x=mean.OD,fill=pheno)) + theme_bw() +
  scale_x_log10()

b <- a +
  geom_histogram(alpha=.5,position = "identity") + facet_grid(~igg)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/od_hist.png")

c <- a + 
  geom_density(alpha=.5,position = "identity") + facet_grid(~igg)
#ggsave("data-raw/raw/unap-tesis/RAFAEL-data/od_dens.png")
  #facet_wrap(transform~measure,scales = "free")

Rmisc::multiplot(b,c,cols = 1)
```

# COVARIATES

```{r}
cova <- readxl::read_xlsx("data-raw/raw/unap-tesis/RAFAEL-data/Base Rafael.xlsx",
                  range = "A1:P59",
                  sheet = 1) %>% 
  slice(-1) %>% 
  dplyr::rename(EDAD="X__1",SEXO="X__2") %>% 
  rename_all(funs(stringr::str_to_lower(.))) %>% 
  dplyr::select(codigo:gametocitos) %>% 
  dplyr::select(codigo,condicion="condición",edad,sexo,comunidad,fiebre)
```

```{r}
covb <- readxl::read_xlsx("data-raw/raw/unap-tesis/RAFAEL-data/DENSIDADES.xlsx",
                  range = "A1:E59",
                  sheet = 1) %>% 
  slice(-1) %>% 
  rename_all(funs(stringr::str_to_lower(.))) %>% 
  dplyr::select(1:4) %>% 
  dplyr::rename(par="parástios/ul de sangre") %>% 
  dplyr::select(-4) %>% 
  dplyr::rename(condicion="condición")
```

#### merge issues

- __4 muestras__ con __incompatibilidad de covariables__ en __`edad`__ y __`sexo`__.
    + prioridad: `parasitemia`
    + retiro de observaciones con __`par`=0__
- ¿variable `condición`?

- SOLVE THIS!! AFFECTS [HERE](#tidy-up)

```{r}
covx <- full_join(cova, covb, by="codigo") 

covx %>% 
  dplyr::count(codigo) %>% arrange(desc(n)) %>% filter(n!=1)

#covx %>% filter(codigo=="2235" | codigo=="3053" | codigo=="9165" | codigo=="9801") %>% arrange(codigo) %>% 
#  dplyr::select(codigo,edad,sexo,par)
cova %>% filter(codigo=="2235" | codigo=="3053" | codigo=="9165" | codigo=="9801") %>% arrange(codigo) %>% 
  dplyr::select(codigo,edad,sexo)
covb %>% filter(codigo=="2235" | codigo=="3053" | codigo=="9165" | codigo=="9801") %>% arrange(codigo) %>% 
  dplyr::select(codigo,par)

# par covariates finale
covf <- covb %>% filter(!(codigo=="2235" & par==0 | codigo=="3053" & par==0 | codigo=="9165" & par==0 | codigo=="9801" & par==0)) %>% 
  dplyr::rename(ID=codigo)
```

```{r, eval=FALSE}
mdo %>% dplyr::count(ID)
```

```{r}
mco <- full_join(mdo,covf,by="ID") %>% #dplyr::count(pheno,condicion)
  dplyr::select(-condicion) %>% 
  mutate(Ab.units_log=log10(Ab.units))
```

#### test covariates

```{r}
mcv <- covx %>% 
  #filter(codigo=="2235" | codigo=="3053" | codigo=="9165" | codigo=="9801") %>% 
  filter(!(codigo=="2235" | 
             codigo=="3053" | 
             codigo=="9165" | 
             codigo=="9801")) %>% 
  #filter(codigo=="2235" | codigo=="3053" | codigo=="9165" | codigo=="9801") %>% 
  arrange(codigo) %>% 
  dplyr::select(codigo,edad,sexo,par) %>% 
  dplyr::rename(ID=codigo) %>% 
  full_join(mdo %>% 
              group_by(ID) %>% 
              slice(1) %>% 
              ungroup()
            ,by="ID") %>% 
  dplyr::select(ID,edad,sexo,par,pheno) %>% 
  #mutate(sexo=forcats::fct_recode(sexo,
  #                                "sex1"="1", "sex0"="0")) %>% 
  mutate(sexo=as.factor(sexo),
         pheno=as.factor(pheno),
         edad=as.numeric(edad))
```

```{r,error=TRUE}
mcv_u <- Hmisc::upData(mcv %>% dplyr::select(-ID),
                         labels = c(edad="Age",
                                    sexo="Sex",
                                    par="Parasite density"
                                    ),
                         units = c(edad="(years)",
                                   par="(par/uL)"
                                   ),
                         levels = list(pheno=list("Asymptomatic"="asymptomatic",
                                                    "Symptomatic"="symptomatic"),
                                       sexo=list("Female"="0",
                                                    "Male"="1") #???
                                       )
                         )

Hmisc::html(Hmisc::contents(mcv_u), maxlevels=10, levelType='table')
```

```{r,error=TRUE}
s1 <- Hmisc::summaryM(sexo + edad + par ~ pheno,
               data=mcv_u,
               overall=FALSE, test=TRUE)

Hmisc::latex(s1, caption='Sample covariates',
      exclude1=TRUE, #npct='both', 
      test=TRUE , prtest="P",file="",
      digits=3, prn=FALSE,
      #prmsd=TRUE, brmsd=TRUE, #msdsize=mu$smaller2, #NOT-EVALUATE if PDF
      middle.bold=TRUE, long = FALSE,
      #legend.bottom = TRUE, #insert.bottom = TRUE, 
      what="%", html = TRUE, 
      width="100%"
      ) #change here for LaTeX PDF
```

```{r}
readr::write_rds(mcv_u,"data/unap-rafa-covar.rds")
```


# SEROLOGICAL CLASSIFICATION

## To Do

- Implement the mean / ROC / mixture models method

## mixtools

### check distributions

```{r,fig.width=7,fig.height=2.5}
a <- mco %>% #filter(igg=="igg3") %>% 
  ggplot(aes(x=Ab.units
             #,fill=pheno
             #,fill=igg
             )) + theme_bw() +
  #scale_x_log10() +
  geom_density(alpha=.5,position = "identity") +
  geom_histogram(aes(x=Ab.units,..density..),
                 alpha=.5,position = "identity") +
  #theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title="AU linear distribution")

b <- mco %>% #filter(igg=="igg3") %>% 
  ggplot(aes(sample=Ab.units
             #,fill=pheno
             #,fill=igg
             )) +
  geom_qq(alpha=.2) +
  geom_qq_line(line.p = c(0.25, 0.75)) +
  labs(title="Gaussian quantile-quantile plot") +
  coord_cartesian(ylim = c(0,2000))

Rmisc::multiplot(a,b,cols = 2)
```


```{r,fig.width=10,fig.height=2.2}
mco %>% 
  ggplot(aes(x=Ab.units
             #,fill=pheno
             )) + theme_bw() +
  #scale_x_log10() +
  geom_density(alpha=.5,position = "identity",adjust= 1/2
               ) + 
  geom_histogram(aes(x=Ab.units,..density..),
                 alpha=.5,position = "identity") + 
  facet_wrap(~igg, scales = "free",ncol = 5) +
  labs(title="AU linear distribution per IgG subtype")
```

### apply mixtools

```{r}
# source: http://tinyheero.github.io/2015/10/13/mixture-model.html
f <- mco %>% mutate(igg=as.factor(igg)) %>% 
  mutate(igg=forcats::fct_relevel(igg,"igg","igg1","igg2","igg3")) %>% .$igg

#library("mixtools")

#' Plot a Mixture Component
#' 
#' @param x Input data
#' @param mu Mean of component
#' @param sigma Standard deviation of component
#' @param lam Mixture weight of component
plot_mix_comps <- function(x, mu, sigma, lam) {
  lam * dnorm(x, mu, sigma)
}

set.seed(1)
#length(levels(f))
#wait <- mco %>% filter(igg=="igg2") %>% 
#  .$Ab.units %>% log()

llmix <- data_frame(igg=as.character(),
                    kpr=as.numeric(),
                    lik=as.numeric())
for (j in 2:3) {
    
    mixmdl_p <- normalmixEM(mco %>% 
                              #filter(igg==levels(f)[i]) %>% 
                              .$Ab.units #%>% log10()
                            , 
                            k = j)
    llmix <- llmix %>% 
      union(data_frame(igg="all",#levels(f)[i],
                       kpr=j,
                       lik=mixmdl_p$loglik))
    
  }
llmix %>% arrange(igg,desc(lik)) %>% mutate(aic=2*kpr-2*lik)

#
llmix <- data_frame(igg=as.character(),
                    kpr=as.numeric(),
                    lik=as.numeric())
for (i in 1:length(levels(f))) {
  
  for (j in 2:3) {
    
    mixmdl_p <- normalmixEM(mco %>% 
                              filter(igg==levels(f)[i]) %>% 
                              .$Ab.units #%>% log10()
                            , 
                            k = j)
    llmix <- llmix %>% 
      union(data_frame(igg=levels(f)[i],
                       kpr=j,
                       lik=mixmdl_p$loglik))
    
  }
  
}
llmix %>% arrange(igg,desc(lik)) %>% mutate(aic=2*kpr-2*lik) #%>% 
  #group_by(igg) %>% filter(lik==max(lik))

#mixmdl <- normalmixEM(wait, k = 3)
#mixmdl$loglik
```

```{r,fig.height=3,fig.width=12}
### FOR ALL AB.UNITS (NO IGG SUBTYPES)
set.seed(1)

#for (i in 1:length(levels(f))) {
  
wait <- mco %>% #filter(igg==levels(f)[i]) %>% 
  .$Ab.units #%>% log10()
mixmdl <- normalmixEM(wait, k = 3)
###
r <- data.frame(x = mixmdl$x) %>%
  ggplot() +
  geom_histogram(aes(x, ..density..), 
                 #binwidth = 1, 
                 #colour = "black", 
                 #fill = "gray",
                 alpha=.5,position = "identity"
                 ) +
  stat_function(geom = "line", 
                fun = plot_mix_comps,
                args = list(mixmdl$mu[1], 
                            mixmdl$sigma[1], 
                            lam = mixmdl$lambda[1]),
                colour = "green", lwd = 1.5) +
  stat_function(geom = "line", 
                fun = plot_mix_comps,
                args = list(mixmdl$mu[2], 
                            mixmdl$sigma[2], 
                            lam = mixmdl$lambda[2]),
                colour = "blue", lwd = 1.5) +
  stat_function(geom = "line", 
                fun = plot_mix_comps,
                args = list(mixmdl$mu[3], 
                            mixmdl$sigma[3], 
                            lam = mixmdl$lambda[3]),
                colour = "red", lwd = 1.5) +
  ylab("Density") +
  xlab("Ab.units") +
  labs(title= paste0("Ab.units: ",
                     #ifelse(levels(f)[i]=="igg","IgG ",
                      #      ifelse(levels(f)[i]=="igg1","IgG1 ",
                        #           ifelse(levels(f)[i]=="igg2","IgG2 ",
                         #                 ifelse(levels(f)[i]=="igg3","IgG3 ","IgG4 ")
                          #                )
                          #         )
                          #  ),
                     "3-component distribution"
                     #,": LogLik=",
                     #mixmdl$loglik %>% format(digits=3)
                     )) 
  #+ scale_x_log10()

####
u <- 0.90 # 90% classification probability

post.df <- as.data.frame(cbind(x = mixmdl$x, mixmdl$posterior)) %>% 
  mutate(comp.12=comp.1+comp.2) %>% # sum probabilities of s+ and s++
  mutate(label = ifelse(comp.3 > u, "s-", 
                        ifelse(comp.12 > u, "s+", "s0"
                               #ifelse(comp.1 > u,"s++","s0")
                               ))) %>% 
  mutate(label=forcats::fct_relevel(label,"s-","s0","s+"#,"s++"
                                    ))

s <- post.df %>% 
  ggplot(aes(x = factor(label))) +
  geom_bar() +
  xlab("Component") +
  ylab("Number of Data Points") +
  labs(title="Classification")

###
t <- post.df %>% 
  ggplot() +
  #geom_line(aes(x,comp.1), colour="green", lwd = 1.5) +
  #geom_line(aes(x,comp.2), colour="blue", lwd = 1.5) +
  geom_line(aes(x,comp.12), colour="blue", lwd = 1.5) +
  geom_line(aes(x,comp.3), colour="red", lwd = 1.5) +
  geom_hline(yintercept = u, col = "black") +
  #geom_vline(xintercept = cutoffs[2], col = "black", lty=3) +
  #geom_vline(xintercept = cutoffs[1], col = "black", lty=3) +
  xlab("Ab.units") +
  ylab("classification probability") +
  labs(title="Cutoff")

Rmisc::multiplot(r,t,s,cols = 3)
  
#}

#sum(mco$Ab.units_log == mixmdl$x)
#length(mixmdl$x)
#sum(!post.df$x==mco$Ab.units_log)

msr <- inner_join(mco %>% rownames_to_column(),
           post.df %>%
             #dplyr::rename(Ab.units_log=x) %>% 
             dplyr::select(#Ab.units_log,
                           label) %>% 
             rownames_to_column(),
           by="rowname") #%>% 
  #mutate(test= Ab.units_log.x==Ab.units_log.y) %>% dplyr::count(test)

msr <- msr[FALSE,] #%>% glimpse()
```

### 2-component

```{r,fig.height=3,fig.width=12,eval=FALSE}

set.seed(1)

for (i in 1:length(levels(f))) {
  
wait <- mco %>% filter(igg==levels(f)[i]) %>% 
  .$Ab.units #%>% log10()
mixmdl <- normalmixEM(wait, k = 3)
###
r <- data.frame(x = mixmdl$x) %>%
  ggplot() +
  geom_histogram(aes(x, ..density..), 
                 #binwidth = 1, 
                 #colour = "black", 
                 #fill = "gray",
                 alpha=.5,position = "identity"
                 ) +
  stat_function(geom = "line", 
                fun = plot_mix_comps,
                args = list(mixmdl$mu[1], 
                            mixmdl$sigma[1], 
                            lam = mixmdl$lambda[1]),
                colour = "red", lwd = 1.5) +
  stat_function(geom = "line", 
                fun = plot_mix_comps,
                args = list(mixmdl$mu[2], 
                            mixmdl$sigma[2], 
                            lam = mixmdl$lambda[2]),
                colour = "blue", lwd = 1.5) +
  #stat_function(geom = "line", 
  #              fun = plot_mix_comps,
  #              args = list(mixmdl$mu[3], 
  #                          mixmdl$sigma[3], 
  #                          lam = mixmdl$lambda[3]),
  #              colour = "green", lwd = 1.5) +
  ylab("Density") +
  xlab("Ab.units") +
  labs(title= paste0(ifelse(levels(f)[i]=="igg","IgG: ",
                            ifelse(levels(f)[i]=="igg1","IgG1: ",
                                   ifelse(levels(f)[i]=="igg2","IgG2: ",
                                          ifelse(levels(f)[i]=="igg3","IgG3 ","IgG4: ")
                                          )
                                   )
                            ),
                     "3-component distribution"
                     #,": LogLik=",
                     #mixmdl$loglik %>% format(digits=3)
                     )) 
  #+ scale_x_log10()

####
u <- 0.90 # 90% classification probability

post.df <- as.data.frame(cbind(x = mixmdl$x, mixmdl$posterior)) %>% 
  mutate(comp.sp=ifelse(mean(comp.2)>100,
                        comp.2+comp.3,
                        comp.1+comp.2)) %>% # sum probabilities of s+ and s++
  mutate(label = ifelse(comp.1 > u, "s-", 
                        #ifelse(comp.sp > u, "s+", "s0"
                        ifelse(comp.2 > u, "s+", "s0"
                               #ifelse(comp.1 > u,"s++","s0")
                               ))) %>% 
  mutate(label=forcats::fct_relevel(label,"s-","s0","s+"#,"s++"
                                    )) 

s <- post.df %>% 
  ggplot(aes(x = factor(label))) +
  geom_bar() +
  xlab("Component") +
  ylab("Number of Data Points") +
  labs(title="Classification")

###
t <- post.df %>% 
  ggplot() +
  #geom_line(aes(x,comp.1), colour="green", lwd = 1.5) +
  geom_line(aes(x,comp.2), colour="blue", lwd = 1.5) +
  #geom_line(aes(x,comp.sp), colour="blue", lwd = 1.5) +
  geom_line(aes(x,comp.1), colour="red", lwd = 1.5) +
  geom_hline(yintercept = u, col = "black") +
  #geom_vline(xintercept = 63.6, col = "black", lty=3) +
  #geom_vline(xintercept = 69.7, col = "black", lty=3) +
  xlab("Ab.units") +
  ylab("classification probability") +
  labs(title="Cutoff")


###
msr_p <- inner_join(mco %>%
                    filter(igg==levels(f)[i]) %>% 
                    rownames_to_column(),
           post.df %>%
             #dplyr::rename(Ab.units_log=x) %>% 
             dplyr::select(#Ab.units_log,
                           label) %>% 
             rownames_to_column(),
           by="rowname") #%>% 
  #mutate(test= Ab.units_log.x==Ab.units_log.y) %>% dplyr::count(test)


msr <- union(msr, msr_p)


Rmisc::multiplot(r,t,s,cols = 3)
  
}
```

### three component distribution

- Possible mistake for __IgG2__ and __IgG4__: 
    + Definition of _S-_ using only the 1st component and _S+_ using the 2nd and 3rd one, as was standardized for all the `igg subtypes`.
    + __CORRECTION__: If 2nd component have a mean AU __lower than an ARBITRARY THRESHOLD__, define _S+_ as the sum of only the 3rd component probabilities.
        - RESULT: a threshold of __100__ give results as expected for `igg2` and `igg4` seropositivity.
            + IN REFERENCE: __Rouhani 2015 (figure 2)__.
            + ACHIEVED CRITERIA: lower proportion of indetermined serology `s0`
        - ALTERNATIVE: a threshold of __40__ may be convinient for `igg4` in order to explain its:
            + significantly higher mean AU in symptomatics, and
            + strong association to symptomatic susceptibility.
            + According to the component CLASSIFICATION CRITERIA based on the proportion of `s0`, which is higher than the previous threshold, this is not the best classification.
            + However, if a boost or other phenomena (including a different genotype within the study population) that increase the proportion of IgG4 during a symptomatic episode is assumed, this may be the right one.
            + Interestingly, this 2nd component is full of symptomatic samples.
    + NOTE: Even though the mean of the 2nd component is higher than 100, for `igg` the threshold of seropositivity is lower than 40 AU, under the right criteria.


```{r,fig.height=3,fig.width=12}
set.seed(1)

# i= 3
for (i in 1:length(levels(f))) {
  
wait <- mco %>% filter(igg==levels(f)[i]) %>% 
  .$Ab.units #%>% log10()
mixmdl <- normalmixEM(wait, k = 3)
###
r <- data.frame(x = mixmdl$x) %>%
  ggplot() +
  geom_histogram(aes(x, ..density..), 
                 #binwidth = 1, 
                 #colour = "black", 
                 #fill = "gray",
                 alpha=.5,position = "identity"
                 ) +
  stat_function(geom = "line", 
                fun = plot_mix_comps,
                args = list(mixmdl$mu[1], 
                            mixmdl$sigma[1], 
                            lam = mixmdl$lambda[1]),
                colour = "red", lwd = 1.5) +
  stat_function(geom = "line", 
                fun = plot_mix_comps,
                args = list(mixmdl$mu[2], 
                            mixmdl$sigma[2], 
                            lam = mixmdl$lambda[2]),
                colour = "blue", lwd = 1.5) +
  stat_function(geom = "line", 
                fun = plot_mix_comps,
                args = list(mixmdl$mu[3], 
                            mixmdl$sigma[3], 
                            lam = mixmdl$lambda[3]),
                colour = "green", lwd = 1.5) +
  ylab("Density") +
  xlab("Ab.units") +
  labs(title= paste0(ifelse(levels(f)[i]=="igg","IgG: ",
                            ifelse(levels(f)[i]=="igg1","IgG1: ",
                                   ifelse(levels(f)[i]=="igg2","IgG2: ",
                                          ifelse(levels(f)[i]=="igg3","IgG3 ","IgG4: ")
                                          )
                                   )
                            ),
                     "3-component distribution"
                     #,": LogLik=",
                     #mixmdl$loglik %>% format(digits=3)
                     )) 
  #+ scale_x_log10()

####
u <- 0.90 # 90% classification probability

post.df <- as.data.frame(cbind(x = mixmdl$x, mixmdl$posterior)) %>% 
  #mutate(comp.12=comp.1+comp.2,
  #       comp.23=comp.2+comp.3) %>% 
  mutate(comp.sp=if_else(rep(mixmdl$mu[2]>40,length(mixmdl$x)), # UMBRAL ARBITRARIO!!
                        comp.2+comp.3, # sero+ equals to the sum of comp 2+3
                        comp.3)) %>% # sero+ equals to the sum of comp 3
  mutate(comp.sn=if_else(rep(mixmdl$mu[2]>40,length(mixmdl$x)),
                        comp.1, # sero- equals to the sum of comp 1
                        comp.1+comp.2)) %>% # sero+ equals to the sum of comp 1+2
  mutate(label = if_else(comp.sn > u, "s-", 
                        ifelse(comp.sp > u, "s+", "s0"
                        #ifelse(comp.2 > u, "s+", #"s0"
                               #ifelse(comp.1 > u,"s++","s0")
                               ))) %>% 
  mutate(label=forcats::fct_relevel(label,"s-","s0","s+"#,"s++"
                                    )) 

s <- post.df %>% 
  ggplot(aes(x = factor(label))) +
  geom_bar() +
  xlab("Component") +
  ylab("Number of Data Points") +
  labs(title="Classification")

###
t <- post.df %>% 
  ggplot() +
  #geom_line(aes(x,comp.1), colour="green", lwd = 1.5) +
  #geom_line(aes(x,comp.2), colour="blue", lwd = 1.5) +
  #geom_point(aes(x,comp.sp), colour="blue", lwd = 1.5) +
  geom_line(aes(x,comp.sp), colour="blue", lwd = 1.5) +
  #geom_point(aes(x,comp.sn), colour="red", lwd = 1.5) +
  geom_line(aes(x,comp.sn), colour="red", lwd = 1.5) +
  geom_hline(yintercept = u, col = "black") +
  #geom_vline(xintercept = 63.6, col = "black", lty=3) +
  #geom_vline(xintercept = 69.7, col = "black", lty=3) +
  xlab("Ab.units") +
  ylab("classification probability") +
  labs(title="Cutoff")


###
msr_p <- inner_join(mco %>%
                    filter(igg==levels(f)[i]) %>% 
                    rownames_to_column(),
           post.df %>%
             #dplyr::rename(Ab.units_log=x) %>% 
             dplyr::select(#Ab.units_log,
                           label) %>% 
             rownames_to_column(),
           by="rowname") #%>% 
  #mutate(test= Ab.units_log.x==Ab.units_log.y) %>% dplyr::count(test)


msr <- union(msr, msr_p)


Rmisc::multiplot(r,t,s,cols = 3)
  
}
```

# DATA FRAME

```{r}
#mco
#msr
```


```{r}
glimpse(
  msr %>% 
  mutate(ord=as.numeric(ord)) %>%  arrange(ord) %>% 
  dplyr::select(-Ab.units_log, -rowname)
)
#summary(mdo)
readr::write_csv(
  msr %>% 
  mutate(ord=as.numeric(ord)) %>%  arrange(ord) %>% 
  dplyr::select(-Ab.units_log, -rowname)
                 ,"data/unap-rafael.csv")
readr::write_rds(
  msr %>% 
  mutate(ord=as.numeric(ord)) %>%  arrange(ord) %>% 
  dplyr::select(-Ab.units_log, -rowname)
  , "data/unap-rafael.rds")
```

# STATISTICAL ANALYSIS PLAN

## To Do

- Do it

## Sample size

```{r}
mco %>% 
  group_by(igg) %>% dplyr::count(pheno)
```


## Test Hypothesis

### Mean AU

```{r,message=FALSE,fig.width=10,fig.height=2.2}
f <- mco %>% mutate(igg=as.factor(igg)) %>% .$igg

#mco <- mco %>% 
#  #dplyr::select(Ab.units,par) %>% 
#  mutate(Ab.units=log10(Ab.units),par=log10(par)) %>% 
#  filter(par!=-Inf) #%>% 

stt <- data_frame(##estimate=as.double(),
                  ##estimate1=as.double(),
                  ##estimate2=as.double(),
                  statistic=as.double(),
                  p.value=as.double(),
                  #parameter=as.double(),#
                  #conf.low=as.double(),#
                  #conf.high=as.double(),#
                  method=as.factor(NULL),
                  alternative=as.factor(NULL),
                  igg=as.character(),
                  lab=as.character())

for (l in 1:length(levels(f))) {
  
## Primero, Prueba de Hipótesis para comparar varianzas:
i <- mco %>% filter(igg==levels(f)[l]) %>% 
  var.test(Ab.units_log ~ pheno, data = .) %>% broom::tidy()
## Rpta: Bajo n.s. 0.05, F cae en Región de no-Rechazo de Hipótesis Nula (RnoRHo)
## Conclusión: Supuesto de igualdad de varianzas poblacionales SÍ es válido
## Segundo, Prueba de Hipótesis para comparar medias:
j <- mco %>% filter(igg==levels(f)[l]) %>% 
  #t.test(Ab.units_log ~ pheno, data = ., var.equal= i$p.value>0.05) %>%
  #wilcox.test(Ab.units_log ~ pheno, data = .) %>%#, 
  wilcox.test(Ab.units ~ pheno, data = .) %>%#, 
              #conf.int=TRUE) %>% 
  broom::tidy() %>%  #%>% format(digits=2)
  dplyr::select(-starts_with("estimate")) %>% 
  mutate(igg=levels(f)[l],
         lab= paste0(#"t=",#should be done -> mix variance equality test
                     #"W=",#non-parametric used in literature
                     #statistic %>% format(digits=2),
                     #", df=",parameter %>% format(digits=2),
                     #", P",ifelse(p.value<0.001,"<0.001",
                     "italic('P')",ifelse(p.value<0.001,"<0.001",
                                  paste0("==",p.value %>% format(digits=2))
                                  )
                     )
         )

stt <- stt %>% union(j)

}

stt <- stt %>% arrange(igg) %>% dplyr::select(igg,everything()) %>% 
  mutate(x=.5,
         y=.15) %>% 
  mutate(igg=forcats::fct_recode(igg,
                                 "IgG"="igg",
                                 "IgG1"="igg1",
                                 "IgG2"="igg2",
                                 "IgG3"="igg3",
                                 "IgG4"="igg4"
                                 ))

# plot
mco %>% #filter(igg==levels(f)[1]) %>% 
  mutate(igg=forcats::fct_recode(igg,
                                 "IgG"="igg",
                                 "IgG1"="igg1",
                                 "IgG2"="igg2",
                                 "IgG3"="igg3",
                                 "IgG4"="igg4"
                                 ),
         pheno=forcats::fct_recode(pheno,
                                   "Asymptomatic"="asymptomatic",
                                   "Symptomatic"="symptomatic")) %>%
  ggplot(aes(pheno,Ab.units)) +
  geom_boxplot(#position=position_dodge(0.8)
  ) +
  geom_dotplot(#aes(fill=sev_WHO), 
    binaxis='y', stackdir='center', alpha=.3, 
    dotsize=1#, position=position_dodge(0.8)
  ) +
  
  facet_wrap(~igg,ncol = 5) + 
  
  geom_text(aes(x,y,label=lab),data = stt,parse = T,
            #vjust=.5,hjust=0.05,size=3.5) +
            vjust=.5,hjust=0.05,size=3.5) + # if log10, then change it.
  
  #coord_cartesian(ylim = c(-300,2000)) + # if log10, then change it.
  scale_y_log10() +
  
  #labs(title="Test AU mean equality among phenotypes per IgG subtypes") +
  xlab("Carrier") + ylab("Antibody units (log scale)")
```

- nota:
    + prueba de hipotesis no-paramétrica
    + ploteo en escala logística

```{r}
msr %>% group_by(igg,pheno) %>% dplyr::summarise(min=min(Ab.units),
                                          q25=quantile(Ab.units) %>% .[2],
                                          mean=mean(Ab.units),
                                          q50=quantile(Ab.units) %>% .[3],
                                          q75=quantile(Ab.units) %>% .[4],
                                          max=max(Ab.units))
```

### Proportion sero+

```{r}
msr %>% dplyr::count(igg,label) %>% 
  group_by(igg) %>% mutate(tot=sum(n),prop= (n*100)/(sum(n))) %>% 
  filter(label=="s+")
```

```{r,fig.width=5.25,fig.height=2.5}
msr %>% 
  mutate(label=forcats::fct_relevel(label,"s-","s+","s0"#,"s++"
                                    )) %>% 
  ggplot(aes(x=igg,fill=label)) +
  geom_bar(position = "fill") +
  #facet_grid(~igg) +
  labs(title="Proportion of seropositives per IgG subtype")
```

```{r}
msr %>% dplyr::count(igg,pheno,label) %>% 
  group_by(igg,pheno) %>% mutate(tot=sum(n),prop= (n*100)/(sum(n))) %>% 
  filter(label=="s+") #%>% 
  #ungroup() %>% 
  #dplyr::select(-n,-label, -tot) %>% 
  #spread(pheno,prop)
```

```{r}
msr %>% 
  dplyr::select(pheno,igg,label) %>% 
  #filter(igg==levels(f)[i]) %>% 
  #dplyr::select(-igg) %>% 
  group_by(igg,pheno,label) %>% dplyr::summarise(n=n()) %>% ungroup() %>% 
  spread(label,n)
```


```{r}
ftt <- data_frame(igg=as.character(),
                  p.value=as.double(),
                  #estimate=as.double(),
                  method=as.character()#,
                  #alternative=as.factor()
                  )


for (i in 1:length(levels(f))) {
  
fst <- msr %>% 
  filter(!label=="s0") %>% 
  dplyr::select(pheno,igg,label) %>% 
  filter(igg==levels(f)[i]) %>% 
  dplyr::select(-igg) %>% 
  group_by(pheno,label) %>% dplyr::summarise(n=n()) %>% ungroup() %>% 
  #spread(label,n) %>% as.matrix() %>% 
  reshape2::acast(pheno ~ label,
                  value.var = "n") #%>% #class()

fst[is.na(fst)] <- 0

ftt <- ftt %>% 
  union(
  fisher.test(fst) %>% broom::tidy() %>% 
    mutate(igg=levels(f)[i]) %>% 
    dplyr::select(igg,p.value,
                  #estimate,
                  method#,alternative
                  )
)


  
}

ftt %>% arrange(igg) %>% 
  mutate(sig=ifelse(p.value<0.05,"signif","not-signif"))
```

```{r}
fttt <- ftt %>% arrange(igg) %>% dplyr::select(igg,everything()) %>% 
  mutate(x=.5,
         y=-.05) %>% 
  mutate(lab= paste0("italic('P')",ifelse(p.value<0.001,"<0.001",
                                  paste0("==",p.value %>% format(digits=2))
                                  )
                     )
         ) %>% 
  mutate(lab=stringr::str_replace(lab,"(P = 0\\...)(.+)","\\1")) %>% 
  mutate(igg=forcats::fct_recode(igg,
                                 "IgG"="igg",
                                 "IgG1"="igg1",
                                 "IgG2"="igg2",
                                 "IgG3"="igg3",
                                 "IgG4"="igg4"
                                 ))
```

```{r,fig.width=11,fig.height=2.2}
msr %>% 
  mutate(igg=forcats::fct_recode(igg,
                                 "IgG"="igg",
                                 "IgG1"="igg1",
                                 "IgG2"="igg2",
                                 "IgG3"="igg3",
                                 "IgG4"="igg4"
                                 ),
         pheno=forcats::fct_recode(pheno,
                                   "Asymptomatic"="asymptomatic",
                                   "Symptomatic"="symptomatic")) %>%
  mutate(label=forcats::fct_relevel(label,"s-","s+","s0"#,"s++"
                                    )) %>% 
  full_join(fttt %>% dplyr::select(igg,lab,y,x),by = "igg") %>% 
  mutate(lab=if_else(label=="s+",lab,NA_character_)) %>% 
  arrange(lab) %>% 
  mutate(lab=if_else(igg==lead(igg),NA_character_,lab)) %>% 
  filter(!label=="s0") %>% 
  ggplot(aes(x=pheno,fill=label)) +
  geom_bar(position = "fill") +
  facet_grid(~igg) +
  
  coord_cartesian(ylim = c(-.12,1)) +
  geom_text(aes(x=x,
                y=y,label=lab),parse = T,#position = "fill",#data = fttt,,
            ##vjust=.5,hjust=0.05,size=3.5) +
            vjust=1,hjust=0.0005,size=3.5
            ) + # if log10, then change it.
  
  #labs(title="Proportion of seropositives among phenotypes per IgG subtype") +
  xlab("Carrier") + ylab("Proportion") +
  scale_fill_grey(#start = 0, end = .9,
                  name="Serology",labels=c("Negative","Positive"#,"Indeterminate"
                                           ))
```

```{r}
##aqui
s1 <- Hmisc::summaryM(label
                      ~ pheno,
               data=msr %>% filter(igg=="igg"),
               overall=FALSE, test=TRUE)

Hmisc::latex(s1, caption='Sample covariates',
      exclude1=TRUE, npct='both', 
      digits=3,
      #prmsd=TRUE, brmsd=TRUE, #msdsize=mu$smaller2, #NOT-EVALUATE if PDF
      middle.bold=TRUE, long = TRUE,
      #legend.bottom = TRUE, #insert.bottom = TRUE, 
      what="%", html = TRUE, width="100%"
      ) #change here for LaTeX PDF
```
```{r}
##aqui
s1 <- Hmisc::summaryM(label
                      ~ pheno,
               data=msr %>% filter(igg=="igg1"),
               overall=FALSE, test=TRUE)

Hmisc::latex(s1, caption='Sample covariates',
      exclude1=TRUE, npct='both', 
      digits=3,
      #prmsd=TRUE, brmsd=TRUE, #msdsize=mu$smaller2, #NOT-EVALUATE if PDF
      middle.bold=TRUE, long = TRUE,
      #legend.bottom = TRUE, #insert.bottom = TRUE, 
      what="%", html = TRUE, width="100%"
      ) #change here for LaTeX PDF
```

```{r}
##aqui
s1 <- Hmisc::summaryM(label
                      ~ pheno,
               data=msr %>% filter(igg=="igg2"),
               overall=FALSE, test=TRUE)

Hmisc::latex(s1, caption='Sample covariates',
      exclude1=TRUE, npct='both', 
      digits=3,
      #prmsd=TRUE, brmsd=TRUE, #msdsize=mu$smaller2, #NOT-EVALUATE if PDF
      middle.bold=TRUE, long = TRUE,
      #legend.bottom = TRUE, #insert.bottom = TRUE, 
      what="%", html = TRUE, width="100%"
      ) #change here for LaTeX PDF
```

```{r}
##aqui
s1 <- Hmisc::summaryM(label
                      ~ pheno,
               data=msr %>% filter(igg=="igg3"),
               overall=FALSE, test=TRUE)

Hmisc::latex(s1, caption='Sample covariates',
      exclude1=TRUE, npct='both', 
      digits=3,
      #prmsd=TRUE, brmsd=TRUE, #msdsize=mu$smaller2, #NOT-EVALUATE if PDF
      middle.bold=TRUE, long = TRUE,
      #legend.bottom = TRUE, #insert.bottom = TRUE, 
      what="%", html = TRUE, width="100%"
      ) #change here for LaTeX PDF
```

```{r}
##aqui
s1 <- Hmisc::summaryM(label
                      ~ pheno,
               data=msr %>% filter(igg=="igg4"),
               overall=FALSE, test=TRUE)

Hmisc::latex(s1, caption='Sample covariates',
      exclude1=TRUE, npct='both', 
      digits=3,
      #prmsd=TRUE, brmsd=TRUE, #msdsize=mu$smaller2, #NOT-EVALUATE if PDF
      middle.bold=TRUE, long = TRUE,
      #legend.bottom = TRUE, #insert.bottom = TRUE, 
      what="%", html = TRUE, width="100%"
      ) #change here for LaTeX PDF
```

<!-- -->

```{r,eval=FALSE, echo=FALSE}
TeaTasting <-
matrix(c(3, 1, 1, 3),
       nrow = 2,
       dimnames = list(Guess = c("Milk", "Tea"),
                       Truth = c("Milk", "Tea")))
fisher.test(TeaTasting, alternative = "greater")
## => p = 0.2429, association could not be established

Job <- matrix(c(1,2,1,0, 3,3,6,1, 10,10,14,9, 6,7,12,11), 4, 4,
dimnames = list(income = c("< 15k", "15-25k", "25-40k", "> 40k"),
                satisfaction = c("VeryD", "LittleD", "ModerateS", "VeryS")))
fisher.test(Job)
fisher.test(Job, simulate.p.value = TRUE, B = 1e5)

class(Job)
```


### Distribution sero+

- the __minimum__ value of the __sero+__ distribution is close to the __cut-off__ and could be assumed as its value, but is not exactly that value.

```{r,fig.width=10.5,fig.height=2.5}
msr %>% 
  mutate(label=forcats::fct_relevel(label,"s-","s+","s0"#,"s++"
                                    )) %>% 
  ggplot(aes(x=Ab.units,fill=label)) +
  #geom_bar(position = "fill") +
  geom_histogram(position = "stack") +
  facet_grid(~igg,scales = "free") +
  labs(title="Proportion of seropositives")
```

```{r}
msr %>% 
  filter(label=="s+") %>% 
  group_by(igg) %>% dplyr::summarise(min=min(Ab.units),
                              q25=quantile(Ab.units) %>% .[2],
                              mean=mean(Ab.units),
                              q50=quantile(Ab.units) %>% .[3],
                              q75=quantile(Ab.units) %>% .[4],
                              max=max(Ab.units))
```

```{r,fig.width=10.5,fig.height=4}
msr %>% 
  mutate(label=forcats::fct_relevel(label,"s-","s+","s0"#,"s++"
                                    )) %>% 
  ggplot(aes(x=Ab.units,fill=label)) +
  #geom_bar(position = "fill") +
  #scale_x_log10() +
  geom_histogram(position = "stack") +
  facet_grid(pheno~igg,scales = "free") +
  labs(title="Proportion of seropositives")
```

## Correlation

### IgG subtypes

```{r,fig.height=4,fig.width=6}
mco %>% dim()

u <- mco %>% filter(pheno=="asymptomatic") %>% 
  dplyr::select(ID,igg,Ab.units) %>% 
  #mutate(Ab.units=log10(Ab.units)) %>% 
  spread(igg,Ab.units) %>% 
  dplyr::select(-ID) %>% 
  mutate(pheno="asymptomatic") %>% 
  mutate(pheno=forcats::fct_recode(pheno,
                                   "Asymptomatic"="asymptomatic",
                                   "Symptomatic"="symptomatic")) %>%
  dplyr::rename("IgG"="igg","IgG1"="igg1",
                "IgG2"="igg2","IgG3"="igg3","IgG4"="igg4")

v <- mco %>% filter(pheno=="symptomatic") %>% 
  dplyr::select(ID,igg,Ab.units) %>% 
  #mutate(Ab.units=log10(Ab.units)) %>% 
  spread(igg,Ab.units) %>% 
  dplyr::select(-ID) %>% 
  mutate(pheno="symptomatic") %>% 
  mutate(pheno=forcats::fct_recode(pheno,
                                   "Asymptomatic"="asymptomatic",
                                   "Symptomatic"="symptomatic")) %>%
  dplyr::rename("IgG"="igg","IgG1"="igg1",
                "IgG2"="igg2","IgG3"="igg3","IgG4"="igg4")

#par(mfrow=c(1,3))

a <- union(u,v) %>% 
  dplyr::select(-pheno) 
a %>% 
  PerformanceAnalytics::chart.Correlation(method = "spearman",main="all",histogram = F)

b <- union(u,v) %>% 
  filter(pheno=="Asymptomatic") %>% 
  dplyr::select(-pheno) 

b %>% 
  PerformanceAnalytics::chart.Correlation(method = "spearman",main="asymptomatic",histogram = F)

c <- union(u,v) %>% 
  filter(pheno=="Symptomatic") %>% 
  dplyr::select(-pheno) 

c %>% 
  PerformanceAnalytics::chart.Correlation(method = "spearman",main="symptomatic",histogram = F)

#a

#par(mfrow=c(1,1))
```

```{r}
#d <- c
#d <- b
#d <- c
#cor(d[which(complete.cases(d)),],method = "spearman")

cor.ext <- function(d) {#d <- a

e <- Hmisc::rcorr(as.matrix(d[which(complete.cases(d)),]), type="spearman")
#e
rr <- e$r %>% as.data.frame() %>% mutate_all(funs(if_else(.==1,NA_real_,.))) %>% 
  gather(ig,vl) %>% mutate(vl=format(vl,digits = 2)) %>% mutate(vl=as.numeric(vl)) %>% 
  group_by(ig) %>% 
  mutate(id=1:n()) %>% 
  spread(ig,vl) %>% dplyr::select(-id) %>% rownames_to_column() #%>% dplyr::select(-rowname)
pp <- e$P %>% as.data.frame() %>% mutate_all(funs(if_else(.<0.001,111,
                                                    if_else(.<0.01,11,
                                                            if_else(.<0.05,1,0))))) %>% 
  mutate_all(as.character) %>% 
  mutate_all(funs(if_else(.=="111","(***)",
                          if_else(.=="11","(**)",
                                  if_else(.=="1","(*)","(ns)"))))) %>% rownames_to_column()

ee <- rr %>% gather(ig,vl) %>% mutate(vl="") %>% group_by(ig) %>% mutate(id=1:n()) %>% spread(ig,vl) %>% dplyr::select(-id) %>% dplyr::select(rowname,everything())

ppp <- as.matrix(pp)
rrr <- as.matrix(rr)
eee <- as.matrix(ee)

#eee <- rrr

eee[1,3] <- paste(rrr[1,3],ppp[1,3])
eee[1:2,4] <- paste(rrr[1:2,4],ppp[1:2,4])
#eee[2,4] <- paste(rrr[2,4],ppp[2,4])
eee[1:3,5] <- paste(rrr[1:3,5],ppp[1:3,5])
#eee[2,5] <- paste(rrr[2,5],ppp[2,5])
#eee[3,5] <- paste(rrr[3,5],ppp[3,5])
eee[1:4,6] <- paste(rrr[1:4,6],ppp[1:4,6])
ext <- eee %>% as_data_frame() %>% mutate(rowname=colnames(.)[-1]) #%>% #as.matrix()
  #dplyr::rename("subtype"=rowname)
return(ext)
  
}

aa <- cor.ext(a) %>% #dplyr::rename("all"=rowname) %>% 
  column_to_rownames() %>% as.matrix() #%>% knitr::kable(format = "latex")
bb <- cor.ext(b) %>% #dplyr::rename("asympt"=rowname) %>% 
  column_to_rownames() %>% as.matrix()
cc <- cor.ext(c) %>% #dplyr::rename("sympt"=rowname) %>% 
  column_to_rownames() %>% as.matrix()

readr::write_rds(aa,"data/cor_all.rds")
readr::write_rds(bb,"data/cor_asymp.rds")
readr::write_rds(cc,"data/cor_sympt.rds")
```

```{r}
aa
bb
cc
#knitr::kable(cc,"latex")
```

```{r}
ax <- t(as.matrix(c("","","","","")))
rownames(ax) <- "All individuals"
colnames(ax) <- c("IgG","IgG1","IgG2","IgG3","IgG4")
bx <- t(as.matrix(c("","","","","")))
rownames(bx) <- "Asymptomatics"
colnames(bx) <- c("IgG","IgG1","IgG2","IgG3","IgG4")
cx <- t(as.matrix(c("","","","","")))
rownames(cx) <- "Symptomatics"
colnames(cx) <- c("IgG","IgG1","IgG2","IgG3","IgG4")
dd <- rbind(ax,aa,bx,bb,cx,cc)[-c(6,12,18),-1]
readr::write_rds(dd,"data/cor_full.rds")
```

```{r,message=FALSE,fig.height=5,fig.width=6}
#mco %>% 
#  dplyr::select(pheno,igg,Ab.units,par) %>% 
#  GGally::ggpairs(mapping = aes(color = pheno))

union(u,v) %>% 
  dplyr::rename("Status"=pheno) %>% 
  GGally::ggscatmat(color = "Status",corMethod = "spearman") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title="Correlation between IgG subtypes per phenotype")# +scale_fill_grey(start = 0, end = .9)

# correlación general y estratificada
#union(u,v) %>% 
#  GGally::ggpairs(mapping = aes(color = pheno))
```

```{r,eval=FALSE,echo=FALSE}
library(desctable)
library(DT)
library(pander)
mtcars %>%
  #dplyr::mutate(am = factor(am, labels = c("Automatic", "Manual"))) %>%
  group_by(am) %>%
  desctable(tests = list(.default = ~wilcox.test,
                         mpg      = ~t.test)) %>%
  pander
```

### parasitemia

```{r,fig.height=2,fig.width=4}
mco %>% 
  ggplot(aes(Ab.units,fill=pheno)) +
  geom_density(position = "identity",alpha=0.6) +
  #geom_rug() + 
  scale_x_log10()
```

```{r,fig.height=2,fig.width=4}
mco %>% #summary(mco$par)
  ggplot(aes(par,fill=pheno)) +
  geom_density(position = "identity",alpha=0.6) +
  #geom_rug() + 
  scale_x_log10()
```

```{r,fig.height=3,fig.width=10}
mco %>% 
  ggplot(aes(Ab.units,par,fill=pheno)) +
  geom_point(aes(colour=pheno)) +
  scale_x_log10() + 
  scale_y_log10() +
  facet_grid(pheno~igg,scales = "free") +
  geom_smooth(aes(colour=pheno),method = lm)
```

```{r,fig.height=4,fig.width=6,eval=FALSE}
u <- mco %>% filter(pheno=="asymptomatic") %>% 
  dplyr::select(ID,igg,Ab.units,par) %>% 
  #mutate(Ab.units=log10(Ab.units)) %>% 
  spread(igg,Ab.units) %>% 
  dplyr::select(-ID)

v <- mco %>% filter(pheno=="symptomatic") %>% 
  dplyr::select(ID,igg,Ab.units,par) %>% 
  #mutate(Ab.units=log10(Ab.units)) %>% 
  spread(igg,Ab.units) %>% 
  dplyr::select(-ID) 

#union(u,v) %>% 
#  PerformanceAnalytics::chart.Correlation(histogram = FALSE,method = "spearman")
```

```{r,fig.width=10,fig.height=3,eval=FALSE}
mco %>% 
  ggplot(aes(Ab.units,par)) +
  geom_point(aes(colour=pheno)) +
  scale_x_log10() +
  scale_y_log10() +
  facet_grid(pheno~igg
             #,scales = "free"
             ) +
  geom_smooth(method = lm)
```

```{r,message=FALSE,fig.width=10,fig.height=3.6}
f <- mco %>% mutate(igg=as.factor(igg)) %>% .$igg
g <- mco %>% mutate(pheno=as.factor(pheno)) %>% .$pheno

#mco <- mco %>% 
#  #dplyr::select(Ab.units,par) %>% 
#  mutate(Ab.units=log10(Ab.units),par=log10(par)) %>% 
#  filter(par!=-Inf) #%>% 

stt <- data_frame(estimate=as.double(),
                  ##estimate1=as.double(),
                  ##estimate2=as.double(),
                  statistic=as.double(),
                  p.value=as.double(),
                  parameter=as.double(),#
                  conf.low=as.double(),#
                  conf.high=as.double(),#
                  method=as.factor(NULL),
                  alternative=as.factor(NULL),
                  igg=as.character(),
                  pheno=as.character(),
                  signf=as.character(),
                  rho=as.character(),
                  lab=as.character())

for (l in 1:length(levels(f))) {
  
  for (k in 1:length(levels(g))) {
    
## Primero,
j <- mco %>% filter(par!=0) %>%  filter(igg==levels(f)[l] & pheno==levels(g)[k]) %>% #l #k
  #t.test(Ab.units_log ~ pheno, data = ., var.equal= i$p.value>0.05) %>%
  #wilcox.test(Ab.units_log ~ pheno, data = .) %>%#, 
              #conf.int=TRUE) %>% 
  cor.test(~ log10(Ab.units) + log10(par), 
           data = ., method = "pearson") %>% 
  broom::tidy() %>%  #%>% format(digits=2)
  #dplyr::select(-starts_with("estimate")) %>% 
  mutate(igg=levels(f)[l],#l
         pheno=levels(g)[k],#k
         signf=if_else(p.value<0.001,"ooo",
                       if_else(p.value<0.01,"oo",
                               if_else(p.value<0.05,"o","ns"#""#
                                       ))),
         rho= estimate %>% format(digits=2),
         lab= paste0(#"t=",#should be done -> mix variance equality test
                     #"W=",#non-parametric used in literature
                     #"S=",f$statistic,", rho="
                     "rho=",estimate %>% format(digits=2),
                     "\nP",ifelse(p.value<0.001,"<0.001",
                                  paste0("=",p.value %>% format(digits=2)))
                     )
         )

#i <- mco %>% filter(igg==levels(f)[l] & pheno==levels(g)[k]) %>% #l #k
#  #t.test(Ab.units_log ~ pheno, data = ., var.equal= i$p.value>0.05) %>%
#  #wilcox.test(Ab.units_log ~ pheno, data = .) %>%#, 
#              #conf.int=TRUE) %>% 
#  cor.test(~ Ab.units + par, 
#           data = ., method = "spearman") %>% 
#  broom::tidy() %>%  #%>% format(digits=2)
#  #dplyr::select(-starts_with("estimate")) %>% 
#  mutate(igg=levels(f)[l],#l
#         pheno=levels(g)[k],#k
#         signf=if_else(p.value<0.001,"(***)",
#                       if_else(p.value<0.01,"(**)",
#                               if_else(p.value<0.05,"(*)","(ns)"))),
#         rho= estimate %>% format(digits=2),
#         lab= paste0(#"t=",#should be done -> mix variance equality test
#                     #"W=",#non-parametric used in literature
#                     #"S=",f$statistic,", rho="
#                     "rho=",estimate %>% format(digits=2)#,
#                     #"\nP",ifelse(p.value<0.001,"<0.001",
#                      #            paste0("=",p.value %>% format(digits=2)))
#                     )
#         )

stt <- stt %>% union(j) #%>% union(i)
    
  }

}

sttt <- stt %>% arrange(igg,pheno) %>% dplyr::select(igg,pheno,everything()) %>% 
  mutate(x=if_else(igg=="igg1"|igg=="igg3"|igg=="igg4",10,
                   if_else(igg=="igg2",.7,.1)),
         y=60000)

# plot
specie_name <- c("asymptomatic"="Asymptomatic","symptomatic"="Symptomatic",
                 "igg"="IgG","igg1"="IgG1","igg2"="IgG2","igg3"="IgG3","igg4"="IgG4"
                 )
mco %>% 
  filter(par!=0) %>% 
  ggplot(aes(Ab.units,par)) +
  geom_point(#aes(colour=pheno)
             ) +
  scale_x_log10() +
  scale_y_log10(limits=c(100,65000)
                ) +
  facet_grid(pheno~igg,labeller = as_labeller(specie_name)
             ,scales = "free_x"
             ) +
  #geom_smooth(method = lm,se=F,col="grey50",lwd=.5) +
  #geom_smooth(method = loess,se=F,col="grey50",lwd=.5) +
  
  geom_text(aes(x,y,label=paste0("r","==",
                                 rho,"^",signf
                                 )),data = sttt, parse = T,
            vjust=1,hjust=0.05,size=3.5) +
  
  #labs(#title="Correlation between AU and parasitemia per IgG subtype and phenotype",
   #    caption="oo: P < 0.01; o: P < 0.05; ns: non-significant"
    #   ) +
  xlab("Antibody units (log scale)") + 
  ylab(expression(paste("Parasite/",mu,"L"," (log scale)"))) #+ #ylab("Parasite/uL") +
  #coord_cartesian(ylim = c(0,20000))
  #scale_color_grey(#start = 0, end = .9,
   #               name="Carrier",labels=c("Asymptomatic","Symptomatic"#,"Indeterminate"
    #                                       ))
```
```{r}
d <- data.frame(x=1:3,y=1:3)
qplot(x, y, data=d) + geom_text(aes(2, 2.5,
              label="rho~and~some~other~text"), parse=TRUE)
```

```{r,message=FALSE,fig.width=10,fig.height=2.2}
f <- mco %>% mutate(igg=as.factor(igg)) %>% .$igg

#mco <- mco %>% 
#  #dplyr::select(Ab.units,par) %>% 
#  mutate(Ab.units=log10(Ab.units),par=log10(par)) %>% 
#  filter(par!=-Inf) #%>% 

stt <- data_frame(estimate=as.double(),
                  ##estimate1=as.double(),
                  ##estimate2=as.double(),
                  statistic=as.double(),
                  p.value=as.double(),
                  #parameter=as.double(),#
                  #conf.low=as.double(),#
                  #conf.high=as.double(),#
                  method=as.factor(NULL),
                  alternative=as.factor(NULL),
                  igg=as.character(),
                  lab=as.character())

for (l in 1:length(levels(f))) {
  
## Primero,
j <- mco %>% filter(igg==levels(f)[l]) %>% #l
  #t.test(Ab.units_log ~ pheno, data = ., var.equal= i$p.value>0.05) %>%
  #wilcox.test(Ab.units_log ~ pheno, data = .) %>%#, 
              #conf.int=TRUE) %>% 
  cor.test(~ Ab.units + par, 
           data = ., method = "spearman") %>% 
  broom::tidy() %>%  #%>% format(digits=2)
  #dplyr::select(-starts_with("estimate")) %>% 
  mutate(igg=levels(f)[l],#l
         lab= paste0(#"t=",#should be done -> mix variance equality test
                     #"W=",#non-parametric used in literature
                     #"S=",f$statistic,", rho="
                     "rho=",estimate %>% format(digits=2),
                     "\nP",ifelse(p.value<0.001,"<0.001",
                                  paste0("=",p.value %>% format(digits=2)))
                     )
         )

stt <- stt %>% union(j)

}

stt <- stt %>% arrange(igg) %>% dplyr::select(igg,everything()) %>% 
  mutate(x=.1,
         y=200)

# plot
mco %>% #filter(igg==levels(f)[1]) %>% 
  ggplot(aes(Ab.units,par)) +
  geom_point() +
  scale_x_log10() +
  scale_y_log10() +
  geom_smooth(method = lm,se=F,col="grey50",lwd=.5) +
  #geom_smooth(method = loess,se=F,col="grey50",lwd=.5) +
  geom_text(aes(x,y,label=lab),data = stt,
            vjust=.5,hjust=0.05,size=3.5) +
  facet_grid(~igg
             #,scales = "free"
             ) +
  #facet_wrap(~igg,ncol = 5)
  labs(title="Correlation between AU and parasitemia per IgG subtype")
```

```{r,eval=FALSE,include=FALSE}
# CHUNK: to compare `pearson cor.test` = `lm` != `spearman cor.test`

#for (l in 1:length(levels(f))) {
## via lm
l=1
mco %>% filter(igg==levels(f)[l] & pheno=="symptomatic") %>% #l=2
  #t.test(Ab.units_log ~ pheno, data = ., var.equal= i$p.value>0.05) %>%
  #wilcox.test(Ab.units_log ~ pheno, data = .) %>%#, 
              #conf.int=TRUE) %>% 
  #cor.test(~ Ab.units + par, method = "spearman",
  lm(par ~ Ab.units, 
           data = .#, method = "spearman"
     ) %>% 
  broom::tidy()

#}
```

### age

```{r}
age <- mco %>% 
  dplyr::select(ID, code, pheno, igg, Ab.units) %>% 
  full_join(mcv,by=c("ID","pheno"))
```

```{r,fig.height=2,fig.width=10}
age %>% #summary(age$edad)
  ggplot(aes(Ab.units,fill=pheno)) +
  geom_density(position = "identity",alpha=0.7) +
  facet_wrap(~igg,scales = "free",ncol = 5) #+scale_x_log10()
```

```{r,fig.height=2,fig.width=4}
age %>% #summary(age$edad)
  ggplot(aes(edad,fill=pheno)) +
  geom_density(position = "identity",alpha=0.7)
```


```{r,message=FALSE,fig.width=10,fig.height=3.6}
f <- age %>% mutate(igg=as.factor(igg)) %>% .$igg
g <- age %>% mutate(pheno=as.factor(pheno)) %>% .$pheno

#mco <- mco %>% 
#  #dplyr::select(Ab.units,par) %>% 
#  mutate(Ab.units=log10(Ab.units),par=log10(par)) %>% 
#  filter(par!=-Inf) #%>% 

stt <- data_frame(estimate=as.double(),
                  ##estimate1=as.double(),
                  ##estimate2=as.double(),
                  statistic=as.double(),
                  p.value=as.double(),
                  #parameter=as.double(),#
                  #conf.low=as.double(),#
                  #conf.high=as.double(),#
                  method=as.factor(NULL),
                  alternative=as.factor(NULL),
                  igg=as.character(),
                  pheno=as.character(),
                  signf=as.character(),
                  rho=as.character(),
                  lab=as.character())

for (l in 1:length(levels(f))) {
  
  for (k in 1:length(levels(g))) {
    
## Primero,
j <- age %>% filter(igg==levels(f)[l] & pheno==levels(g)[k]) %>% #l #k
  #t.test(Ab.units_log ~ pheno, data = ., var.equal= i$p.value>0.05) %>%
  #wilcox.test(Ab.units_log ~ pheno, data = .) %>%#, 
              #conf.int=TRUE) %>% 
  cor.test(~ Ab.units + edad, 
           data = ., method = "spearman") %>% 
  broom::tidy() %>%  #%>% format(digits=2)
  #dplyr::select(-starts_with("estimate")) %>% 
  mutate(igg=levels(f)[l],#l
         pheno=levels(g)[k],#k
         signf=if_else(p.value<0.001,"ooo",
                       if_else(p.value<0.01,"oo",
                               if_else(p.value<0.05,"o","ns"#""#
                                       ))),
         rho= estimate %>% format(digits=2),
         lab= paste0(#"t=",#should be done -> mix variance equality test
                     #"W=",#non-parametric used in literature
                     #"S=",f$statistic,", rho="
                     "rho=",estimate %>% format(digits=2),
                     "\nP",ifelse(p.value<0.001,"<0.001",
                                  paste0("=",p.value %>% format(digits=2)))
                     )
         )

#i <- age %>% filter(igg==levels(f)[l] & pheno==levels(g)[k]) %>% #l #k
#  #t.test(Ab.units_log ~ pheno, data = ., var.equal= i$p.value>0.05) %>%
#  #wilcox.test(Ab.units_log ~ pheno, data = .) %>%#, 
#              #conf.int=TRUE) %>% 
#  cor.test(~ Ab.units + edad, 
#           data = ., method = "spearman") %>% 
#  broom::tidy() %>%  #%>% format(digits=2)
#  #dplyr::select(-starts_with("estimate")) %>% 
#  mutate(igg=levels(f)[l],#l
#         pheno=levels(g)[k],#k
#         lab= paste0(#"t=",#should be done -> mix variance equality test
#                     #"W=",#non-parametric used in literature
#                     #"S=",f$statistic,", rho="
#                     "rho=",estimate %>% format(digits=2),
#                     "\nP",ifelse(p.value<0.001,"<0.001",
#                                  paste0("=",p.value %>% format(digits=2)))
#                     )
#         )

stt <- stt %>% union(j) #%>% union(i)
    
  }

}

sttu <- stt %>% arrange(igg,pheno) %>% dplyr::select(igg,pheno,everything()) %>% 
  mutate(x=.1,
         y=78)

# plot
specie_name <- c("asymptomatic"="Asymptomatic","symptomatic"="Symptomatic",
                 "igg"="IgG","igg1"="IgG1","igg2"="IgG2","igg3"="IgG3","igg4"="IgG4"
                 )
age %>% 
  ggplot(aes(Ab.units,edad)) +
  geom_point(#aes(colour=pheno)
             ) +
  #scale_x_log10() +
  #scale_y_log10() +
  facet_grid(pheno~igg,labeller = as_labeller(specie_name)
             ,scales = "free_x"
             ) +
  #geom_smooth(method = lm,se=F,col="grey50",lwd=.5) +
  #geom_smooth(method = loess,se=F,col="grey50",lwd=.5) +
  geom_text(aes(x,y,label=paste0("rho","==",
                                 rho,"^",signf
                                 )),data = sttu, parse = T,
            vjust=.5,hjust=0.05,size=3.5) +
  #labs(title="Correlation between AU and parasitemia per IgG subtype and phenotype") +
  xlab("Antibody units") + ylab("Age (years)") +
  coord_cartesian(ylim = c(0,85))
  #scale_color_grey(#start = 0, end = .9,
   #               name="Carrier",labels=c("Asymptomatic","Symptomatic"#,"Indeterminate"
    #                                       ))
```

```{r,message=FALSE,fig.width=10,fig.height=2.2}
f <- age %>% mutate(igg=as.factor(igg)) %>% .$igg

#mco <- mco %>% 
#  #dplyr::select(Ab.units,par) %>% 
#  mutate(Ab.units=log10(Ab.units),par=log10(par)) %>% 
#  filter(par!=-Inf) #%>% 

stt <- data_frame(estimate=as.double(),
                  ##estimate1=as.double(),
                  ##estimate2=as.double(),
                  statistic=as.double(),
                  p.value=as.double(),
                  #parameter=as.double(),#
                  #conf.low=as.double(),#
                  #conf.high=as.double(),#
                  method=as.factor(NULL),
                  alternative=as.factor(NULL),
                  igg=as.character(),
                  lab=as.character())

for (l in 1:length(levels(f))) {
  
## Primero,
j <- age %>% filter(igg==levels(f)[l]) %>% #l
  #t.test(Ab.units_log ~ pheno, data = ., var.equal= i$p.value>0.05) %>%
  #wilcox.test(Ab.units_log ~ pheno, data = .) %>%#, 
              #conf.int=TRUE) %>% 
  cor.test(~ Ab.units + edad, 
           data = ., method = "spearman") %>% 
  broom::tidy() %>%  #%>% format(digits=2)
  #dplyr::select(-starts_with("estimate")) %>% 
  mutate(igg=levels(f)[l],#l
         lab= paste0(#"t=",#should be done -> mix variance equality test
                     #"W=",#non-parametric used in literature
                     #"S=",f$statistic,", rho="
                     "rho=",estimate %>% format(digits=2),
                     "\nP",ifelse(p.value<0.001,"<0.001",
                                  paste0("=",p.value %>% format(digits=2)))
                     )
         )

stt <- stt %>% union(j)

}

stt <- stt %>% arrange(igg) %>% dplyr::select(igg,everything()) %>% 
  mutate(x=.1,
         y=55)

# plot
age %>% #filter(igg==levels(f)[1]) %>% 
  ggplot(aes(Ab.units,edad)) +
  geom_point() +
  scale_x_log10() +
  #scale_y_log10() +
  geom_smooth(method = lm,se=F,col="grey50",lwd=.5) +
  #geom_smooth(method = loess,se=F,col="grey50",lwd=.5) +
  geom_text(aes(x,y,label=lab),data = stt,
            vjust=.5,hjust=0.05,size=3.5) +
  facet_grid(~igg
             #,scales = "free"
             ) +
  #facet_wrap(~igg,ncol = 5)
  labs(title="Correlation between AU and parasitemia per IgG subtype")
```


## Association

- outcome: symptomatic P.f. malaria

### On Power and sample size

#### minimum number of candidate predictors

```{r}
case=20
ctrl=24
m_c=min(case,ctrl) #limiting sample size
p=5 #current number of predictors
#round(m_c/15) #
p<m_c/15
```

- Given the current _limiting sample size_, for a reliable model the number of __candidate predictors must be lower than `r round(m_c/15)`.__

#### minimum number of cases

```{r}
k=5 #number of covariates
p= 0.4 # prevalence of outcome
n_x=10*k/p
```

- Given the __measured covariates__ and __prevalence__ of modeled outcome, the __minimum sample size required is `r n_x`__.

```{r}
# exposition: symptomatic malaria (60% asympt in Pf)
#
# given the proportion (prevalence) relevant OR and required power, 
# obtain the sample size required
p=0.40 #proportion of exposure in general population OR outcome provalence
#(OR=pA*(1-pB)/pB/(1-pA)) # 2
OR=.5 #hypothetical OR
#
kappa=1 # sampling ratio between case:control
alpha=0.05 #type 1 error (false positives)
beta=0.20 #power=1-beta #type 2 error (false negatives)
#(n= (((1+kappa)^2)*((qnorm(1-alpha/2)+qnorm(1-beta))^2)) / (kappa*p*(1-p)*((log(OR))^2)) )
n1= (4*((qnorm(1-alpha/2)+qnorm(1-beta))^2)) / (p*(1-p)*((log(OR))^2)) 
#ceiling(n) # Afridi 220, Bragga 263, Stanisic 206, Medeiros 28+24
```

- Given the known __prevalence__ of symptomatic malaria in Iquitos and a relevant __Odds Ratio__ reported in literature, a __sample size of `r ceiling(n1)`__ would be required to achieve a __power 80%__.

```{r}
# given the available sample size and obtained Odds Ratio, 
# obtain the actual power
n2= 44
#(OR=.5)
z=log(OR)*sqrt(n2)/sqrt(((1+kappa)^2)/(kappa*p*(1-p)))
Power=pnorm(z-qnorm(1-alpha/2))+pnorm(-z-qnorm(1-alpha/2))
```

- Following the same method, given the __current sample size__, this analysis have a __power of `r format(Power*100,digits = 4)`%__.

```{r,eval=FALSE,echo=FALSE}
#http://powerandsamplesize.com/Calculators/Test-Odds-Ratio/Equality
# TEST ODDD EQUALITY BETWEEN TWO GROUPS
pA=0.40 #probability of the outcome in group A
pB=0.25 #probability of the outcome in group B
kappa=1 # sampling ratio
alpha=0.05 #error rate
beta=0.20 #power=1-beta
(OR=pA*(1-pB)/pB/(1-pA)) # 2
(nB=(1/(kappa*pA*(1-pA))+1/(pB*(1-pB)))*((qnorm(1-alpha/2)+qnorm(1-beta))/log(OR))^2)
ceiling(nB) # 156
z=log(OR)*sqrt(nB)/sqrt(1/(kappa*pA*(1-pA))+1/(pB*(1-pB)))
(Power=pnorm(z-qnorm(1-alpha/2))+pnorm(-z-qnorm(1-alpha/2)))
```


### Tidy up

#### inmuno

```{r}
#library(tidyverse)

mmo <- mco %>% #filter(igg=="igg") %>% 
  mutate(pheno=as.factor(pheno)) %>% 
  mutate(pheno=forcats::fct_relevel(pheno,"symptomatic")) %>% 
  mutate(pheno.num=ifelse(pheno=="asymptomatic",0,1)) %>% 
  mutate(pheno.log=ifelse(pheno=="asymptomatic",FALSE,TRUE))
```

- the only reads available for sample `2235` are for the asymptomatic state!

```{r,eval=FALSE}
cova %>% 
covb %>% 
covx %>% 
  filter(codigo=="2235" | codigo=="3053" | codigo=="9165" | codigo=="9801") %>% 
  arrange(codigo) #%>% 
  #dplyr::select(codigo,edad,sexo,par,everything())
```

```{r,eval=FALSE}
## add covariates
full_join(mmo,mcv,by=c("ID","par","pheno")) %>% 
  filter(ID=="2235" | ID=="3053" | ID=="9165" | ID=="9801") %>% 
  arrange(ID)
```

#### covariates

- NOTA: 2235 tiene dos observaciones en matriz de covariables, pero en templates solo ha sido identificado como asintomático!

```{r}
mcv_t <- mcv %>% #filter(igg=="igg") %>% 
  mutate(pheno=as.factor(pheno)) %>% 
  mutate(pheno=forcats::fct_relevel(pheno,"symptomatic")) %>% 
  mutate(pheno.num=ifelse(pheno=="asymptomatic",0,1)) %>% 
  mutate(pheno.log=ifelse(pheno=="asymptomatic",FALSE,TRUE))
```


#### inmuno per subclass

```{r}
### MULTIPLE LOGISTIC REGRESSION
mmo_x <- mmo %>% 
  dplyr::select(ID,
                #code,
                #pheno,
                igg
                ,Ab.units
                #,Ab.units_log
                ,pheno.num#,pheno.log # 1: symptomatic, 0:: asymptomatic
                ) %>% 
  spread(igg
         ,Ab.units
         #,Ab.units_log
         )

#mmo_x.omit = na.omit(mmo_x)
```

#### add covariates

```{r}
mlr <- mcv_t %>% 
  dplyr::select(ID,pheno.num, edad, sexo, par) %>% 
  inner_join(mmo_x,by=c("ID","pheno.num"))

mlr_na = na.omit(mlr)

#dd <- rms::datadist(mlr_na); options(datadist='dd')
```

```{r}
# https://stackoverflow.com/questions/7505547/detach-all-packages-while-working-in-r

detachAllPackages <- function() {

  basic.packages <- c("package:stats",
                      "package:graphics",
                      "package:grDevices",
                      "package:utils",
                      "package:datasets",
                      "package:methods",
                      "package:base")

  package.list <- search()[
    ifelse(
      unlist(
        gregexpr("package:",search())
        )==1,TRUE,FALSE
      )
    ]

  package.list <- setdiff(package.list,basic.packages)

  if (length(package.list)>0)  
    for (package in package.list) 
      detach(package, character.only=TRUE)

}

detachAllPackages()
```


```{r, message=FALSE}
# EXPLAINED: https://stats.stackexchange.com/questions/64788/interpreting-a-logistic-regression-model-with-multiple-predictors

library(tidyverse)
mmo_x_rms <- mlr_na %>% 
  mutate(igg_c=ntile(igg,3),
         igg1_c=ntile(igg1,3),
         igg2_c=ntile(igg2,3),
         igg3_c=ntile(igg3,3),
         igg4_c=ntile(igg4,3)
         ) %>% 
  mutate(igg_c=as.factor(igg_c),
         igg1_c=as.factor(igg1_c),
         igg2_c=as.factor(igg2_c),
         igg3_c=as.factor(igg3_c),
         igg4_c=as.factor(igg4_c)) %>% 
  mutate(igg_d=igg/10,
         igg1_d=igg1/10,
         igg2_d=igg2/10,
         igg3_d=igg3/10,
         igg4_d=igg4/10) %>% 
  mutate(igg_l=log10(igg),
         igg1_l=log10(igg1),
         igg2_l=log10(igg2),
         igg3_l=log10(igg3),
         igg4_l=log10(igg4)) #%>% 
  #dplyr::select(ID,pheno.num,
   #             igg=igg_c,
    #            igg1=igg1_c,
     #           igg2=igg2_c,
      #          igg3=igg3_c,
       #         igg4=igg4_c
        #        )

dd <- rms::datadist(mmo_x_rms); options(datadist='dd')
```

#### data with missings

```{r}
mmo %>% dplyr::count(igg)
```

### Univariate

#### PRE: univariate

```{r,eval=FALSE,echo=FALSE}
trm <- data_frame(igg=as.character(),
                  term=as.character(),
                  estimate=as.double(),
                  std.error=as.double(),
                  statistic=as.double(),
                  p.value=as.double())

dvm <- data_frame(igg=as.character(),
                  null.deviance= as.double(),
                  df.null= as.integer(),
                  logLik= as.double(),
                  AIC= as.double(),
                  BIC= as.double(),
                  deviance= as.double(),
                  df.residual= as.integer())

aov <- data_frame(igg=as.character(),
                  Resid..Df=as.double(),
                  Resid..Dev=as.double(),
                  df=as.double(),
                  Deviance=as.double(),
                  p.value=as.double())

for (i in 1:length(levels(f))) {
  
model <- glm(pheno ~ Ab.units_log,
            data= mmo %>% filter(igg==levels(f)[i]),
            family = binomial(link="logit"))

trm <- trm %>% 
  union(
    broom::tidy(model) %>% 
      mutate(igg=levels(f)[i]) %>% 
      dplyr::select(igg,term,everything())
  )

dvm <- dvm %>% 
  union(
    broom::glance(model) %>% 
      mutate(igg=levels(f)[i]) %>% 
      dplyr::select(igg,everything())
  )

broom::glance(model) # TEST AIC

aov <- aov %>% 
  union(
    anova(model,
          update(model, ~1),
          test="Chisq") %>% 
      broom::tidy() %>% 
      mutate(igg=levels(f)[i]) %>% 
      dplyr::select(igg,everything())
  )

}

#dvm %>% arrange(igg)
#trm %>% arrange(igg,term) %>% #.$term
#  filter(term!="(Intercept)") %>% 
#  dplyr::select(-term)
#aov %>% arrange(igg,Resid..Df)
```

```{r,eval=FALSE,echo=FALSE}
#summary(model)
#model$coefficients
#confint(model)
exp(model$coefficients)         # exponentiated coefficients
exp(confint(model))             # 95% CI for exponentiated coefficients
```

```{r,fig.width=10,fig.height=2.5,eval=FALSE,echo=FALSE}
#model = glm(pheno ~ Ab.units,
#            data= mmo,
#            family = binomial(link="logit"))
ggplot(mmo, aes(x=Ab.units, y=pheno.num)) + 
  geom_point(alpha=.3) + 
  stat_smooth(method="glm", method.args=list(family="binomial"), se=TRUE) +
  facet_grid(~igg,scales = "free") +
  #scale_x_log10() + #better model by means of AIC
  labs(title= expression(paste("Antibody responses associated with susceptibility to symptomatic malaria"))) +
  #to protection against
  #with susceptibility to
  ylab("Probability")
```

##### age

```{r,eval=FALSE,echo=FALSE}
mod1 = rms::lrm(as.factor(pheno.num) ~ edad
                ,data=mmo_x_rms, x=TRUE, y=TRUE)
mod1
paste("AIC=",AIC(mod1))
summary(mod1)
#plot(summary(mod1), log=TRUE,main = "log Odds Ratio",cex.main = 0)
```

##### sex

```{r,eval=FALSE,echo=FALSE}
mod1 = rms::lrm(as.factor(pheno.num) ~ sexo
                ,data=mmo_x_rms, x=TRUE, y=TRUE)
mod1
paste("AIC=",AIC(mod1))
summary(mod1)
#plot(summary(mod1), log=TRUE,main = "log Odds Ratio",cex.main = 0)
```

##### parasitemia

```{r,eval=FALSE,echo=FALSE}
mod1 = rms::lrm(as.factor(pheno.num) ~ par #+ igg1 + igg2 + igg3 + igg4
                ,data=mmo_x_rms, x=TRUE, y=TRUE)
mod1
paste("AIC=",AIC(mod1))
summary(mod1)
```

##### igg

```{r,eval=FALSE,echo=FALSE}
mod1 = rms::lrm(as.factor(pheno.num) ~ igg #+ igg1 + igg2 + igg3 + igg4
                ,data=mmo_x_rms, x=TRUE, y=TRUE)
mod1
paste("AIC=",AIC(mod1))
summary(mod1)
```

##### igg1

```{r,eval=FALSE,echo=FALSE}
mod1 = rms::lrm(as.factor(pheno.num) ~ igg1 #+ igg1 + igg2 + igg3 + igg4
                ,data=mmo_x_rms, x=TRUE, y=TRUE)
mod1
paste("AIC=",AIC(mod1))
summary(mod1)
```

##### igg2

```{r,eval=FALSE,echo=FALSE}
mod1 = rms::lrm(as.factor(pheno.num) ~ igg2 #+ igg1 + igg2 + igg3 + igg4
                ,data=mmo_x_rms, x=TRUE, y=TRUE)
mod1
paste("AIC=",AIC(mod1))
summary(mod1)
```

##### igg3

```{r,eval=FALSE,echo=FALSE}
mod1 = rms::lrm(as.factor(pheno.num) ~ igg3 #+ igg1 + igg2 + igg3 + igg4
                ,data=mmo_x_rms, x=TRUE, y=TRUE)
mod1
paste("AIC=",AIC(mod1))
summary(mod1)
```

##### igg4

```{r,eval=FALSE,echo=FALSE}
mod1 = rms::lrm(as.factor(pheno.num) ~ igg4 #+ igg1 + igg2 + igg3 + igg4
                ,data=mmo_x_rms, x=TRUE, y=TRUE)
mod1
paste("AIC=",AIC(mod1))
summary(mod1)
```

##### IgG model

```{r,eval=FALSE,echo=FALSE,include=FALSE}
### MY PROCEDURE
model = glm(pheno.num ~ igg + igg1 + igg2 + igg3 + igg4 + edad + sexo
            ,data= mmo_x_rms,
            family = binomial(link="logit")#,na.action = na.omit
            )

#summary(model)

#broom::augment(model)
paste("AIC=",AIC(model))
#broom::glance(model) # TEST AIC
#broom::tidy(model)

#model$coefficients
#confint(model)
exp(model$coefficients)         # exponentiated coefficients # ODDS RATIO
exp(confint(model))             # 95% CI for exponentiated coefficients
```

```{r,fig.height=2.2,fig.width=3.8,eval=FALSE,echo=FALSE,include=FALSE}
#cut(seq(.01,1,by=.001),breaks = 3)
#quantile(seq(.01,.1,by=.001))
#quantile(seq(.01,.1,by=.001),probs = c(0,0.33,0.66,1))
GGally::ggcoef(model,exponentiate = T,
               exclude_intercept = T,
               errorbar_height = .25,
               mapping = aes(x = estimate, y = term#, 
                             #size = p.value, 
                             #colour=p.value
                             )) +
  #scale_size_continuous(trans = "reverse"#,
                        #labels = as.numeric(quantile(seq(.01,.1,by=.001),
                        #                             probs = c(0,0.33,0.66,1))
                        #                    )
                        #,limits = c(.1,.001)
   #                     ) +
  #scale_colour_viridis_c(trans = "reverse"#,
                        #labels = as.numeric(quantile(seq(.01,.1,by=.001),
                        #                             probs = c(0,0.33,0.66,1))
                        #                    )
                        #,limits = c(.1,.001)
    #                    ) +
  #scale_x_log10() +
  #coord_trans(x="log10") +
  #coord_cartesian(xlim = c(-10,100)) +
  labs(title="Susceptibility to symptomatic malaria") +
  #Protection against
  #Susceptibility to
  #Lower risk to 
  xlab("odds ratio (log scale)") +
  ylab("antibody subtype") +
  theme(#legend.position=c(.11, .84),
        legend.margin = margin(0,0,0,0),
        legend.title = element_text(size=10),
        legend.key.height = unit(.8,"line")) #+
  #scale_x_continuous(#brakes= c(),
   #                  label = c("0.1",#"1","10",
    #                           "100",#"1000",
     #                          "100000"))
```

##### stepwise selection

```{r,eval=FALSE,echo=FALSE}
### TUTORIAL

model.null = glm(pheno.num ~ 1
            ,data= mmo_x_rms,
            family = binomial(link="logit")#,na.action = na.omit
            )

model.full = glm(pheno.num ~ igg + igg1 + igg2 + igg3 + igg4 + par + edad + sexo
            ,data= mmo_x_rms,
            family = binomial(link="logit")#,na.action = na.omit
            )

step(model.null,
     scope = list(upper=model.full),
             direction="both",
             test="Chisq",
             data=mmo_x_rms)
```

```{r,eval=FALSE,echo=FALSE,include=FALSE}
model.final = glm(pheno.num ~ igg + igg1 + igg3 + igg4 + edad + sexo
            ,data= mmo_x_rms,
            family = binomial(link="logit")#,na.action = na.omit
            )
#summary(model.final)
#rcompanion::nagelkerke(model.final) #nagelkerke 0.492
#lmtest::lrtest(model.final) #0.0001
#plot(fitted(model.final),rstandard(model.final)) #OK
#summary(model.final)$deviance / summary(model.final)$df.residual #1.01 < 1.5
```


###### selected model

```{r,eval=FALSE,echo=FALSE,include=FALSE}
model = model.final

#summary(model)

paste("AIC=",AIC(model))
#broom::augment(model)
#broom::glance(model) # TEST AIC
#broom::tidy(model)

#model$coefficients
#confint(model)
exp(model$coefficients)         # exponentiated coefficients # ODDS RATIO
exp(confint(model))             # 95% CI for exponentiated coefficients
```

```{r,fig.height=2.2,fig.width=3.8,eval=FALSE,echo=FALSE,include=FALSE}
#cut(seq(.01,1,by=.001),breaks = 3)
#quantile(seq(.01,.1,by=.001))
#quantile(seq(.01,.1,by=.001),probs = c(0,0.33,0.66,1))
GGally::ggcoef(model,exponentiate = T,
               exclude_intercept = T,
               errorbar_height = .25,
               mapping = aes(x = estimate, y = term#, 
                             #size = p.value, 
                             #colour=p.value
                             )) +
  scale_size_continuous(trans = "reverse"#,
                        #labels = as.numeric(quantile(seq(.01,.1,by=.001),
                        #                             probs = c(0,0.33,0.66,1))
                        #                    )
                        ##,limits = c(.1,.001)
                        ) +
  scale_colour_viridis_c(trans = "reverse"#,
                        #labels = as.numeric(quantile(seq(.01,.1,by=.001),
                        #                             probs = c(0,0.33,0.66,1))
                        #                    )
                        #,limits = c(.1,.001)
                        ) +
  #scale_x_log10() +
  #coord_trans(x="log10") +
  #coord_cartesian(xlim = c(-10,100)) +
  labs(title="Susceptibility to symptomatic malaria") +
  #Protection against
  #Susceptibility to
  #Lower risk to 
  xlab("odds ratio (log scale)") +
  ylab("antibody subtype") +
  theme(#legend.position=c(.11, .84),
        legend.margin = margin(0,0,0,0),
        legend.title = element_text(size=10),
        legend.key.height = unit(.8,"line")) #+
  #scale_x_continuous(#brakes= c(),
   #                  label = c("0.1",#"1","10",
    #                           "100",#"1000",
     #                          "100000"))
```


# References